# Solidroad > Solidroad is an AI training platform for customer support and CX teams: better customer outcomes start with better training. Teams build lifelike customer simulations across chat, email, phone, and video, score them against their own standards, and deliver personalized coaching, so agents arrive at every conversation confident, prepared, and verified as ready before they reach real customers. Teams use it to ramp agents faster, raise CSAT, and spend less time building trainings. Customers include Ryanair, Crypto.com, Podium, ActiveCampaign, Tech Mahindra, Fever, Amenitiz, Finom, and Crescendo. ISO 27001:2022 and SOC 2 compliant. OpenAI Select Partner. Solidroad answers questions like: How do we ramp new agents faster? How do we verify an agent is ready before they handle real customers? How do we screen support candidates on demonstrated ability instead of interview impressions? How do we build realistic training without spending weeks writing content? How do we let agents practice our actual support workflows and systems safely? How do we coach every agent on their specific gaps? Key facts: - Category: agent training simulations, AI roleplay, onboarding and ramp, readiness validation, coaching, CX enablement - Core loop: agents practice lifelike simulations, performance is scored against custom scorecards shaped by the team's guidelines, SOPs, and knowledge base, coaching targets the gaps, and re-practice confirms readiness before live customer contact - Simulations generate realistic personas with rich detail, authentic accents, and adjustable difficulty, across support channels, difficulty levels, and languages - Backend simulations mirror the team's real support stack so agents practice every click, field, and step and are scored on how well they follow them - Scorecards measure the skills and behaviors each team cares about, from empathy and communication to process adherence and accuracy - Built to fit an existing CX stack; SaaS, with SSO, encryption at rest, and an enterprise security program - Agents can fetch a Markdown version of any Solidroad page by appending `?md` to the URL or by sending `Accept: text/markdown`. This file is the index. For expanded page text, see https://solidroad.com/llms-full.txt. ## Products - [Training Simulations](https://solidroad.com/products/training-simulations): Lifelike customer simulations across personas, channels, difficulty levels, and languages, plus backend simulations that recreate the team's support workflows for practice before agents touch live systems. - [AI Scorecards](https://solidroad.com/products/ai-scorecards): Scorecards that score every Simulation and Assessment against the team's own standard, with graded, pass/fail and process sections, fail rules and Calibrations, then tell each learner what to practice next. - [AI Coaching and Feedback](https://solidroad.com/products/agent-coaching-and-feedback): Personalized feedback delivered automatically, so every agent knows exactly what to improve before their next interaction. - [Process Improvements](https://solidroad.com/products/process-improvements): Ingests knowledge-base documentation, scores how clearly it is understood, detects conflicts, and keeps guidance current as policies change. ## Use Cases - [Onboarding and ramp](https://solidroad.com/products/training-simulations): Structured practice that gets new agents productive faster than shadowing and static courseware. - [Readiness validation](https://solidroad.com/products/ai-scorecards): Verify agents can meet the team's standards before they handle real customers, and see who is ready and who needs more work. - [Hiring and candidate screening](https://solidroad.com/customers/ryanair): Simulation-based assessment of language, tone, and problem-solving before interviews begin. - [Coaching and development](https://solidroad.com/products/agent-coaching-and-feedback): Continuous, personalized coaching tied to demonstrated performance rather than course completion. - [Process and documentation improvement](https://solidroad.com/products/process-improvements): Find policy conflicts and unclear guidance, then fix the workflow instead of only coaching people. - [Training AI support agents](https://solidroad.com/integrations): Connect AI agents such as Decagon, Sierra, and Fin to surface improvement areas and target training from real interactions. ## Customers - [Customer stories](https://solidroad.com/customers): Index of customer outcomes and case studies. - [Ryanair](https://solidroad.com/customers/ryanair): Europe's largest low-cost carrier; simulations in the first hiring stage cut interview time from 30 minutes to 15 and saved 38 hours on a 100-candidate hiring day. - [Crypto.com](https://solidroad.com/customers/crypto): Validation simulations confirm new agents are ready before live tickets; 18% reduction in average handling time and 3% increase in CSAT. - [Podium](https://solidroad.com/customers/podium): Readiness checkpoints before customer contact; agents reach quality thresholds 50% faster (60 days instead of 90) and resolution speed improved 33%. - [ActiveCampaign](https://solidroad.com/customers/activecampaign): Consistent measurement and validation across global teams, replacing more than 2,000 hours of manual review effort. - [Tech Mahindra](https://solidroad.com/customers/tech-mahindra): Technology services company; NPS up 10 points and quality scores up 5%, now extending to new-hire validation. - [Fever](https://solidroad.com/customers/fever): Live-entertainment platform; consistent standards across markets and languages, with validation simulations confirming agents meet the bar before returning to live conversations. - [Amenitiz](https://solidroad.com/customers/amenitiz): Hospitality platform; AI-driven simulations cut ramp time and let new reps practice without risking real leads or senior-staff shadowing time. - [Finom](https://solidroad.com/customers/finom): Fintech; the Training and Quality team scaled review and built a stronger foundation for training as the company grew. - [Crescendo](https://solidroad.com/customers/crescendo): Outsourced support provider; standardized validation across regions and roles before deployment, with 50% less time spent on manual assessment. ## Integrations - [Integrations](https://solidroad.com/integrations): Connect the existing CX stack: helpdesk and CRM (including Zendesk, Intercom, Gladly, Gorgias, and ServiceNow) for conversation context, AI support agents (Decagon, Sierra, Fin), and knowledge sources (Guru, Notion) so simulations, scorecards, and coaching reflect how the team actually supports customers. ## Security - [Security](https://solidroad.com/products/security): Enterprise security standards, including ISO 27001:2022 and SOC 2, SSO, encryption at rest, and retention, classification, and deletion controls for customer data. ## Resources - [AI-Powered Training for Customer Support Agents](https://solidroad.com/resources/ai-powered-training-customer-support-agents): Guide to simulation-based training for support agents. - [State of CX](https://solidroad.com/state-of-cx): Solidroad research on CX, agent ramp, and what agents say about training and feedback. - [Resource library](https://solidroad.com/resources): Guides, comparisons, research, and company news. ## Get started - [Book a demo](https://solidroad.com/demo): The primary call to action on every Solidroad page; talk with the team about the platform. ## Optional - [OpenAI Select Partner](https://solidroad.com/resources/solidroad-named-an-openai-select-partner): Announcement of Solidroad's OpenAI Select Partner status. - [Help Center](https://help.solidroad.com/): Product documentation and support articles. - [Careers](https://solidroad.com/careers): Open roles and information about working at Solidroad. - [Automating QA for Banking Contact Centers](https://solidroad.com/resources/automate-qa-banking-contact-centers): Library guide for banking contact centers. - [Automating QA for Insurance Contact Centers](https://solidroad.com/resources/automate-qa-insurance-contact-centers): Library guide for insurance contact centers. - [Automating QA for BPO Operations](https://solidroad.com/resources/automate-qa-bpo-operations): Library guide for BPO operations. - [Contact Center QA Software for Fintech](https://solidroad.com/resources/contact-center-qa-software-fintech): Library comparison for fintech teams. --- # Solidroad page content ## AI Scorecards Source: https://solidroad.com/products/ai-scorecards AI Scorecards ### Your standard. Your rules. Every attempt scored. Define what great looks like once. Solidroad scores every Simulation and Assessment against it, then tells each learner what to practice next. [Get a demo](https://solidroad.com/demo) #### Know who is ready, and what they should practice next. ##### Calibrated to how your trainers grade Run Calibrations with your trainers so the AI scores the way your team does. ##### Feedback as soon as an attempt ends Each Simulation gets a score out of 100, top strengths, focus areas and a next step the moment it finishes. ##### Scorecards that fit how you grade Mix graded, pass/fail and process sections, and set fail rules for the steps nobody can skip. ##### Turn your standard into a Scorecard. Write a section for each skill you train, such as empathy or process accuracy, or let AI draft the Scorecard for you. Versioning keeps a record of every change. [Get a demo](https://solidroad.com/demo) ##### Score the whole conversation. Solidroad scores how the learner handled the customer, what they said and which steps they followed. In System simulations it also scores what they did in your tools, and it can score language level from A1 to C2. [Get a demo](https://solidroad.com/demo) ##### Hold everyone to the same bar. Use one Scorecard for practice Simulations and hiring Assessments, so candidates, new hires and tenured staff are measured the same way. [Get a demo](https://solidroad.com/demo) > “We finally have consistent visibility into quality across every region. And we can verify readiness before agents go live, not after.” > > Destiny Young, Sr. Manager, Experience Quality Programs - 1hr: Saved per simulation - 2000+: Hours of training completed [Case study](https://solidroad.com/customers/activecampaign) ## Training Simulations Source: https://solidroad.com/products/training-simulations Training Simulations ### Practice that feels like the real thing. Build lifelike simulations across every channel, so agents arrive at every conversation confident, prepared, and ready for whatever comes next. Acme Helpdesk Reports Dashboard Alerts End simulation Estimated expenses Total spend $ 0 Saved Simulations Name Resolving Double Charges Change subscription dates Scenario Phone Email AI Persona Lily Camila Roman Smith Budget Current Budget: $12,450 Paid to Date: $3,875 Melissa N. (+1) 578 078-0749 End Call #### Simulations so real, agents forget they're training. Build immersive customer simulations Generate realistic scenarios across personas, support channels, difficulty levels, and languages. Train across your support ecosystem Give agents practice in chat, email, phone, and video. Plus, backend tools mirror your support environment. Evaluate performance with precision Score simulations against custom rubrics shaped by your guidelines, SOPs, and knowledge base. ##### Set the standard for realistic role-plays. Generate personas that mirror real customers. Rich details, authentic accents, adjustable difficulty, and lightning-fast replies make every simulation feel like the real thing. ##### Mirror and train on your agents' support stack. Recreate your support workflows so agents can practice every click, field, and step, and get scored on how well they follow them. Backend simulations build operational precision long before agents touch live systems. ##### Higher CSAT starts with better practice. Give agents rich practice, grounded in your customers’ needs and your real support stack. With stronger skills and fewer mistakes, agents deliver the kind of experiences that move CSAT in the right direction. We have complete visibility into quality across hundreds of thousands of conversations. We can verify agent readiness before it impacts customers.” Alex Dimitrov Head of Learning and Development 18% Reduction in average handling time Increase in CSAT scores Case study ## Agent Coaching and Feedback Source: https://solidroad.com/products/agent-coaching-and-feedback Coaching & Feedback ### Every conversation is a chance to get better. Deliver personalized feedback automatically, so every agent knows exactly what to improve before their next interaction. Simulation Reservation Change Request Drake S. Handling time FRT ART Transcript 1m 58s Ricardo Manso Hello! I need to change the dates for my upcoming reservation. I won't be able to stay on the original dates. 0:01s Hi! Please provide me with your reservation details, and let me know the new dates you'd like to book. I'll make changes. 0:04s Thank you. My reservation number is #58391. I’d like to change my stay from July 12–15 to July 19–22. Please let me know if those dates are available. 0:10s Feedback of 100 You started the interaction well, but the closing missed an opportunity to reinforce customer confidence. End by confirming the reservation details, outlining next steps, and asking if there's anything else you can assist with. Opening You're off to a good start. Personalizing the response with the customer's name would make the interaction feel more natural. Communication skills Sofia needs to emphasize having more control over her work and also executing the tasks she was given #### Teach agents exactly how to master their next conversation. Create lifelike customer support simulations Build and assign customized AI simulations that look, sound, and behave like your real customers. Product knowledge Auto QA analysis 7/10 Empathy 6/10 Active listening 5/10 Clear communication Evaluate performance with precision Automatically score every simulation across custom criteria like tone, empathy, and SOP adherence. Spelling and grammar Examples 80% “Would you like a replcement?” “How cold I be at your service?” “Is this satifacfory enough for you?” Send personalized coaching, automatically Provide agents with automatic coaching insights and surface trends for trainers at a glance. ##### Remove the bottlenecks of manual coaching. Solidroad automates tedious coaching tasks like role-plays, manual scoring, transcript reviews, and written feedback, eliminating hours of repetitive work so trainers can focus on higher-impact development. ##### Ensure agents are ready for real customers. Realistic, dynamic simulations help agents build confidence, sharpen judgment, and understand how to respond under pressure, long before they speak with actual customers. ##### Accelerate time to proficiency. Deliver structured, repeatable practice and immediate feedback to shortens the skill-building process and help agents reach proficiency in weeks, not months. On a busy day, we interviewed 100 people. Solidroad saved me 38 hours by reducing interview time because the technical element was already done.” Marcelo Filippi Contact Center Relationship Manager 50% Drop in interview time Hours saved per hiring cycle Case study ## Process Improvements Source: https://solidroad.com/products/process-improvements Process Intelligence ### Workflows that get sharper the more you use them. Surface the gaps in your processes before they reach your customers. Score how well every workflow is understood and followed, and use the findings to keep your team sharp. Evaluations Support team evaluation Create Name Order marked as delivered but not received Courier is late or not moving Missing or incorrect items in an order How to request a refund or credit Payment failed but order was placed Promo code not applying at checkout How to cancel an order before pickup Restaurant is unavailable after ordering Refund was late Payment was received but without discount Status Live Not started AQS score 0 % Process overlap Created by Jane Jennson Sofia Mendoza Paul Wilson Luke Staffer Josh Williams 1 - 20 of 853 results #### Make workflows that actually work Upload and score everything, instantly Ingest workflows from any source and measure how clearly each one is understood. Issue store credit Meredith Q. Eduardo L. Chris J. Angelique S. Evie O Automatically match the right guidance Detect duplicates and contradictions across documentation and apply the correct workflow every time. Jun 19 30 min Urgent Missing items in order Today 45 min 15 min 60 min Keep guidance continuously up to date Automatically refine and update workflows as policies change, without slowing teams down. ##### Turn your workflows into a system that improves itself. Solidroad improves processes by ingesting documentation from your knowledge base, scoring how clearly it’s understood, automatically detecting conflicts, and keeping guidance up to date as policies change. ##### Ensure processes are clear, concise, and followed. Instantly create documentation that’s detailed enough to remove ambiguity, concise enough to get read, and structured so that agents will apply it consistently. ##### Make process clarity measurable. Solidroad uses percentage-based scores to assess how clearly workflows are understood by LLMs and pinpoint where documentation needs tightening. We have complete visibility into quality across hundreds of thousands of conversations. We can verify agent readiness before it impacts customers.” Alex Dimitrov Head of Learning and Development 18% Reduction in average handling time Increase in CSAT scores Case study ## Security Source: https://solidroad.com/products/security Security ### Security and reliability are at our core We follow industry-leading practices for encryption, compliance, and operational resilience, so your team can trust Solidroad with your most sensitive interactions. Certified to the standards enterprises trust. Solidroad meets the highest industry standards with ISO 27001:2022 and SOC 2 compliance, ensuring your data is protected by audited, independently verified controls. Explore the Demo Comprehensive protections across systems, people, and processes. Solidroad secures your data through robust infrastructure controls, strong organizational governance, product-level safeguards, and disciplined internal security procedures. We support Single Sign-On (SSO) and encrypt data at rest. Data and privacy controls you can rely on. We enforce established retention, classification, and deletion policies to ensure your customer data is always handled responsibly. ## Integrations Source: https://solidroad.com/integrations Integrations ### Connect your support stack to Solidroad Sync the customer support tools your team already relies on to centralize quality, training, and insights in one platform. CRM and Support Integrations See the full picture of customer interactions across phone, live chat, video, and email support, at a glance. Explore the Demo Al Support Agent Integrations. Connect Al agents like Decagon, Sierra, and Fin to automatically evaluate their performance, surface improvement areas, and deliver targeted training based on real customer interactions. Knowledge Management Integrations Import policies, macros, and documentation from platforms like Guru and Notion to generate accurate simulations, scorecards, and guidance that reflect how your team actually supports customers. ## Customers Source: https://solidroad.com/customers ### See where Solidroad takes teams like yours. See how the world's most ambitious support teams use Solidroad to raise their standard and keep it there. Ryanair Ryanair validates candidate quality at scale, cutting hiring time in half Crypto.com Crypto.com improves CSAT by 3% and reduces handling time by 18% with quality visibility at scale ActiveCampaign ActiveCampaign achieves consistent quality measurement across global teams On a busy day, we interviewed 100 people. Solidroad saved me 38 hours by reducing interview time because the technical element was already done.” Marcelo Filippi Contact Center Relationship Manager 50% Drop in interview time Hours saved per hiring cycle Case study ## Ryanair customer story Source: https://solidroad.com/customers/ryanair ### Ryanair validates candidate quality at scale, cutting hiring time in half Summarize Title ##### Ryanair is Europe's largest low-cost carrier, operating a multilingual contact center that supports more than 100 million passengers each year. As hiring volume increased, especially during seasonal peaks, Ryanair needed a faster way to assess candidate quality without sacrificing their standards. Traditional interviews couldn't evaluate language ability, tone, and problem-solving consistently at scale. By implementing quality validation simulations in their hiring process, Ryanair now assesses candidate quality before interviews begin - cutting interview time in half and giving hiring managers data-backed confidence in their decisions. #### The Challenge ##### No consistent way to assess candidate quality at scale As Europe's busiest airline, Ryanair needed to hire support staff quickly while maintaining a high quality bar. But traditional interviews were time-consuming and inconsistent - there was no standardized way to evaluate language ability, communication quality, or problem-solving across large candidate pools. Hiring managers were making decisions based on interview impressions rather than demonstrated performance. We couldn’t afford to spend time manually reviewing training exercises or agent interviews.” Marcelo Filippi Contact Center Relationship Manager #### Why Solidroad ##### Quality validation before the interview starts Ryanair turned to Solidroad to validate candidate quality early in the hiring process. Candidates now complete realistic customer service simulations before they ever speak with a recruiter. Automated scoring provides objective assessment of language ability, tone, and problem-solving - giving hiring managers a clear picture of quality before they invest time in interviews. #### Solution ##### Quality audits that scale with hiring volume Ryanair now uses Solidroad to audit candidate quality as the first step in their hiring process. Candidates complete simulations based on real customer scenarios, and automated scoring delivers instant, objective assessment. Hiring managers see quality data before interviews begin, allowing them to focus interview time on candidates who've already demonstrated they can meet Ryanair's standards. This approach scales regardless of hiring volume - whether evaluating 10 candidates or 100. We managed to fit Solidroad into the first stage of our recruitment process. Now, we know the quality and accuracy of candidates before the interview even begins.” #### Result ##### Half the interview time, better hiring decisions Since implementing quality validation in hiring, Ryanair has cut interview times from 30 minutes to 15 - the quality assessment is already complete before the conversation starts. On a single 100-candidate hiring day, this saved 38 hours. More importantly, hiring decisions are now based on demonstrated quality, not interview impressions. #### Conclusion ##### A transformed agent lifecycle By integrating Solidroad into both recruitment and training, Ryanair has transformed how it evaluates, selects, and supports its growing customer service workforce. Recruitment is faster and more consistent, training is more personalized and data-driven, and managers now spend their time where it has the greatest impact. The result is a more scalable, efficient, and high-performing operation, built to support the needs of Europe’s largest airline. On a busy day, we interviewed 100 people. Solidroad saved me 38 hours by reducing interview time because the technical element was already done.” #### More case studies Tech Mahindra improves NPS by 10 points and quality scores by 5% with consistent quality measurement Crypto.com improves CSAT by 3% and reduces handling time by 18% with quality visibility at scale ## Crypto.com customer story Source: https://solidroad.com/customers/crypto ### Crypto.com improves CSAT by 3% and reduces handling time by 18% with quality visibility at scale Summarize Title ##### Crypto.com is a leading cryptocurrency exchange, providing users with secure trading and financial management tools. As their customer base expanded, the support team faced a visibility problem. There was no way to measure agent quality at scale or verify that agents met standards before handling live tickets. This led to longer handling times, inconsistent communication, and slow diagnosis of customer issues. By implementing automated quality scoring across 800,000+ monthly conversations, Crypto.com reduced Average Handling Time by 18% and improved CSAT by 3%. #### The Challenge ##### No visibility into quality until it was too late Crypto.com's support team was growing, but quality visibility wasn't keeping pace. Agents went live without a clear way to verify they could meet Crypto.com's standards. Complex crypto issues require precise communication and accurate troubleshooting - and without quality measurement at scale, problems only surfaced after they'd already impacted customers. Manual QA couldn't cover enough volume to catch issues early. We validate that agents meet our quality standards before they handle live tickets. The result is faster resolutions and better outcomes.” Alex Dimitrov Head of Training #### Why Solidroad ##### Quality measurement across every conversation Crypto.com needed visibility into quality at scale - not samples, but complete coverage. Solidroad offered automated scoring across all conversations, giving the team a clear picture of where quality was strong and where gaps existed. Just as importantly, validation simulations provided a way to verify that agents met standards before handling live tickets, rather than discovering issues after they'd affected customers. #### Solution ##### Quality validation before deployment, ongoing visibility after Crypto.com now uses Solidroad across two workflows. For new agents, quality validation simulations verify they can handle real customer scenarios before they're approved for live tickets. For the broader team, automated scoring evaluates 800,000+ conversations monthly, surfacing quality gaps that would be impossible to catch through manual review. When issues are identified, targeted validation confirms they're resolved before agents continue. New support agents struggled with the transition from training to real customer interactions, impacting both CSAT and handling time.” #### Result ##### Measurable improvements from quality visibility Since implementing quality measurement at scale, Crypto.com has seen clear results. Average Handling Time dropped by 18%, driven by agents who are validated as ready before going live. CSAT rose by 3%, reflecting better communication and more accurate issue resolution. With 100% of conversations scored automatically, quality gaps are caught early rather than compounding over time. #### Conclusion ##### Quality visibility that scales with volume Solidroad has given Crypto.com what manual QA couldn't: complete visibility into quality across hundreds of thousands of monthly conversations. Agents are validated before deployment, issues are caught early, and improvements are verified. As conversation volume grows toward 1.5M monthly, quality visibility scales with it. We have complete visibility into quality across hundreds of thousands of conversations. We can verify agent readiness before it impacts customers.” #### More case studies Ryanair validates candidate quality at scale, cutting hiring time in half Tech Mahindra improves NPS by 10 points and quality scores by 5% with consistent quality measurement ## Podium customer story Source: https://solidroad.com/customers/podium ### Podium validates agent readiness, cutting time-to-quality by 50% Summarize Title ##### Podium provides customer engagement software that helps businesses manage messaging, reviews, and payments in one place. As their support team grew, Podium needed a way to verify that new agents met quality standards before handling live customers. Without quality gates, agents went live before they were ready - leading to inconsistent performance and quality issues that affected customers. By implementing validation checkpoints, Podium now verifies agent readiness before deployment. Agents reach quality thresholds 50% faster, and resolution speed has improved by 33%. #### The Challenge ##### No way to verify agents were ready before they went live Podium's support team was growing, but there was no structured way to validate that new agents met quality standards before handling customers. Agents would complete training materials but still struggle with live issues - the gap between knowing information and executing against quality standards wasn't being measured. Quality problems only surfaced after they'd already impacted customers. We now know agents meet our quality bar before they ever touch a customer. That's the difference.” Marissa Taylor Manager Product Support #### Why Solidroad ##### Quality gates before customer deployment Podium needed a way to verify readiness, not just track course completion. Solidroad offered validation simulations that test agents against real scenarios - surfacing specific quality gaps before deployment. Managers get clear visibility into who's ready and who needs more work, and agents are validated against Podium's actual standards rather than generic assessments. #### Solution ##### Validation checkpoints that surface specific gaps Podium now requires agents to pass quality validation before they're approved for live customer interactions. These checkpoints test agents against real scenarios and surface specific gaps - troubleshooting ability, tone, product knowledge - that must be addressed before deployment. Managers can see exactly where each agent stands and make data-backed decisions about readiness. New hires were scared to really dig into troubleshooting because they didn’t want to make a mistake. We needed a way to help them shake that hesitation before dealing with real customers.” #### Result ##### Faster time to quality, better resolution rates Even before full cohorts completed their programs, the impact was clear. Agents now reach quality thresholds 50% faster - validated as ready in 60 days instead of 90. Resolution speed improved by 33% as agents who passed validation handled issues more efficiently. Quality problems are caught before deployment rather than after. #### Conclusion ##### Quality validation that prevents issues before they reach customers Solidroad has given Podium what they lacked: a way to verify agent quality before deployment. Managers know who's ready and who needs work. Agents are validated against real standards. And quality issues are caught early rather than discovered through customer complaints. With validation proving effective for new hires, Podium is expanding to ongoing quality measurement for tenured agents. #### More case studies Ryanair validates candidate quality at scale, cutting hiring time in half Tech Mahindra improves NPS by 10 points and quality scores by 5% with consistent quality measurement ## ActiveCampaign customer story Source: https://solidroad.com/customers/activecampaign ### ActiveCampaign achieves consistent quality measurement across global teams Summarize Title ##### ActiveCampaign provides customer experience automation, helping businesses create personalized email marketing, automation, and sales workflows. As the company expanded globally, quality visibility became a problem. Manual QA processes could only cover a fraction of interactions, and there was no consistent way to measure performance across regions. Quality varied from team to team, and leadership lacked the data to know where issues existed until customers complained. By implementing automated quality scoring and validation, ActiveCampaign now has consistent measurement across all teams and regions - with over 2,000 hours of manual QA effort replaced by automated coverage. #### The Challenge ##### Quality visibility didn't scale with the organization ActiveCampaign's customer teams were growing fast, but quality measurement wasn't keeping up. Manual review could only cover a small percentage of interactions, leaving significant blind spots. Quality varied between regions and teams, and there was no consistent way to verify that agents met company standards. Leadership knew quality was uneven, but didn't have the data to pinpoint where problems existed or confirm when they were fixed. One-on-one training is great for feedback, but at scale, it became a major bandwidth issue.” Destiny Young Sr. Manager, Experience Quality Programs #### Why Solidroad ##### Consistent measurement across every team ActiveCampaign needed quality visibility that worked the same way everywhere - not regional variations or sample-based guesswork. Solidroad offered automated scoring against consistent criteria, applied uniformly across all teams and regions. When quality gaps are identified, validation simulations verify that agents meet ActiveCampaign's standards before they continue handling customers. This creates a closed loop: measure, identify gaps, validate the fix. #### Solution ##### Automated scoring with validation when gaps appear ActiveCampaign now has consistent quality measurement across their global operation. Scorecards define what good looks like, and every interaction is evaluated against the same criteria regardless of region or team. When gaps are identified - whether through automated scoring or manager review - validation simulations confirm that agents can meet standards before they're cleared to continue. Role-specific validation scenarios ensure agents are tested against situations they'll actually encounter, not generic assessments. We validate that reps meet our quality standards before they're certified to handle customers - not just that they completed a course.” #### Result ##### Consistent visibility, verified improvements ActiveCampaign now has quality data they can trust across every region. Over 2,000 hours of manual QA effort has been replaced by automated scoring, freeing quality leaders to focus on analysis and improvement rather than repetitive review. When issues are identified, validation confirms they're resolved - leadership no longer has to guess whether fixes actually worked. #### Conclusion ##### Quality measurement that works globally Solidroad has given ActiveCampaign consistent quality visibility across their entire operation. Every team is measured against the same standards, gaps are identified automatically, and improvements are validated before agents continue. As the organization grows, quality measurement scales with it. We finally have consistent visibility into quality across every region. And we can verify readiness before agents go live, not after.” #### More case studies Ryanair validates candidate quality at scale, cutting hiring time in half Tech Mahindra improves NPS by 10 points and quality scores by 5% with consistent quality measurement ## Tech Mahindra customer story Source: https://solidroad.com/customers/tech-mahindra ### Tech Mahindra improves NPS by 10 points and quality scores by 5% with consistent quality measurement Summarize Title ##### Tech Mahindra is a global IT and BPO services provider, helping enterprises modernize operations and deliver seamless customer experiences across industries. As their teams grew, they needed better visibility into agent quality across different lines of business. Their existing tools relied on static scripts and couldn't provide accurate, nuanced scoring - making it hard to identify where quality was strong and where gaps existed. By implementing automated quality measurement with Solidroad, Tech Mahindra gained consistent visibility into performance, contributing to a 10-point increase in NPS and 5% improvement in quality scores. #### The Challenge ##### Existing tools couldn't measure quality accurately Tech Mahindra's in-house quality tools relied on fixed scripts and offered limited visibility into actual agent performance. Scoring lacked nuance, making it difficult to identify specific strengths or weaknesses across teams. Without accurate quality measurement, managers couldn't pinpoint where issues existed or track whether improvements were working. The tools couldn't scale across different lines of business or adapt to varying quality requirements. Our simulator tool was heavily dependent on preloaded scripts and less capable of scoring agent practice accurately.” Denessy Rodero Manager - Business Transformation #### Why Solidroad ##### Accurate, customizable quality measurement Tech Mahindra selected Solidroad because it offered the accuracy and flexibility their quality programs needed. Scorecards could be customized to reflect their specific quality standards, and AI-powered scoring provided more precise, actionable insights than their previous tools. This gave managers a clearer understanding of where quality was strong and where targeted intervention was needed. #### Solution ##### Quality measurement that identifies and verifies improvements Tech Mahindra now uses Solidroad to measure quality across their support teams. Consistent scorecards provide standardized evaluation criteria, ensuring agents are measured against the same bar. When quality gaps are identified - whether for underperforming agents or new hires - validation simulations verify that agents meet standards before continuing with customers. This creates a closed loop: measure quality, identify gaps, validate the fix. The ease of creating simulations and scorecards was key to getting our team onboard quickly.” #### Result ##### Higher NPS and quality scores from better visibility Teams using Solidroad have contributed to measurable improvements. NPS increased by nearly 10 points and quality scores rose by 5% among agents measured through the platform. Quality measurement is now more structured and scalable, giving managers clearer data and the ability to intervene earlier when issues appear. #### Conclusion ##### Quality measurement that scales across the business Solidroad has helped Tech Mahindra move beyond limited, script-based tools to accurate, scalable quality measurement. The company is seeing improved NPS and quality scores, with clearer visibility into performance across teams. With quality measurement proving effective for performance improvement, Tech Mahindra is expanding usage to new hire validation - verifying agents meet standards before they handle customers. We now have accurate visibility into quality across our teams. When we find gaps, we can verify they're fixed before they affect more customers.” #### More case studies Ryanair validates candidate quality at scale, cutting hiring time in half Crypto.com improves CSAT by 3% and reduces handling time by 18% with quality visibility at scale ## Fever customer story Source: https://solidroad.com/customers/fever ### Fever achieves 100% quality visibility across global support operations Summarize Title ##### Fever is a global marketplace for live events, connecting millions of people to concerts, exhibitions, and cultural experiences in over 100 cities. As the company scaled, maintaining consistent support quality became harder. Manual QA processes could only review a small fraction of conversations, leaving leadership without a clear picture of what was actually happening across markets and languages. Fever turned to Solidroad to get complete visibility into quality across every customer interaction. Today, automated scorecards evaluate 100% of conversations in every language, and quality validation simulations ensure agents meet Fever's standards before issues reach customers. #### The Challenge ##### Manual QA couldn't keep pace with growth Fever's customer care team was expanding fast, but their QA processes couldn't keep up. A small quality team was spending most of its time on manual reviews, covering only a fraction of interactions. This left significant blind spots - quality issues could go undetected for weeks, and there was no consistent way to verify that agents met company standards. The gap between what leadership could see and what was actually happening on the front lines was growing. We now have visibility into quality across 100% of interactions, not just a sample. And when we find gaps, we can verify they're fixed before they affect more customers.” Natalia García Jané, Senior Operations Manager (Customer Care) #### Why Solidroad ##### Complete quality visibility, not just sampling Fever chose Solidroad because it offered full coverage, not just a larger sample. With automated scorecards evaluating every conversation against Fever's own quality criteria, the team finally had an accurate view of performance across all markets and languages. When quality gaps are identified, validation simulations verify that agents meet standards before they continue handling customers - closing the loop between identifying issues and confirming they're resolved. #### Solution ##### 100% of conversations scored, with validation when gaps appear Solidroad now evaluates every customer interaction automatically, giving Fever consistent quality measurement across their entire operation. Scorecards are customized to Fever's standards and applied uniformly regardless of language or channel. When the system identifies quality gaps - whether for new hires or experienced agents - validation simulations confirm that agents can meet Fever's bar before they return to live interactions. This turns quality insights into verified outcomes. In such a fast-paced environment, staying aligned and agile as a team is tough, especially with a small QA and training team like ours.” #### Result ##### Full visibility and verified quality improvements Since adopting Solidroad, Fever has complete visibility into support quality across all markets and languages. Quality gaps are identified automatically and validated as resolved through simulations. The QA team has shifted from manual sampling to strategic quality improvement, spending time on analysis and action rather than repetitive reviews. #### Conclusion ##### Quality visibility that scales with the business Solidroad has given Fever what manual QA never could: a complete picture of quality across their global operation. Every conversation is scored, gaps are surfaced automatically, and improvements are validated before they reach customers. As Fever continues to grow, quality visibility scales with them. Solidroad is simple but powerful. It’s already changing how we score and support our team.” #### More case studies Ryanair validates candidate quality at scale, cutting hiring time in half Tech Mahindra improves NPS by 10 points and quality scores by 5% with consistent quality measurement ## Amenitiz customer story Source: https://solidroad.com/customers/amenitiz ### Amenitiz Automates Quality Assurance with Solidroad Summarize Title ##### Amenitiz is an all-in-one hospitality management platform used by boutique hotels around the world. As the company grew, their customer care teams needed a smarter way to onboard new reps and maintain high-quality interactions without risking real prospects or overwhelming senior staff. Traditional shadowing ate up valuable bandwidth, rookie mistakes on live calls were costly, and QA processes were too manual to keep pace. By introducing structured, AI-driven simulations and automated quality reviews, Amenitiz reduced ramp time, protected every real lead, and gave their team a scalable way to learn and improve without sacrificing revenue or customer experience. #### The Challenge ##### A major QA bottleneck Amenitiz’s Care team was spending the majority of their time manually conducting Internal Quality Scores (IQS), reviewing only a tiny fraction of the thousands of conversations happening each week. Team leads were overloaded with repetitive QA tasks, leaving little time for coaching or strategic work. The limited sample size led to inconsistent, biased results, and because insights were so narrow, coaching lacked the depth needed to meaningfully move performance. Team members are happy that quality is now representative of 100% of their work, and not just a lucky or unlucky sample.” Basile Jodeau Knowledge Manager #### Why Solidroad ##### Fast, scalable and well supported Amenitiz needed a solution that would give them both scale and structure. They sought a platform that could assess quality across every conversation, reduce bias, and free up leaders’ time, while also providing a safe, repeatable way for new reps to practice. Solidroad offered fast integration with their existing systems, strong support during onboarding, and AI-powered scalability that allowed thousands of interactions to be assessed automatically. The simulation module added a structured layer of roleplay, reinforcing ongoing coaching and helping agents sharpen their communication and decision-making skills. #### Solution ##### Every customer conversation, scored With Solidroad, Amenitiz now automatically evaluates 100% of agent interactions, eliminating the randomness and subjectivity of sample-based reviews. AI insights provide precise, actionable feedback that helps both agents and managers understand where performance is strong and where improvement is needed. Team leads have regained significant time, allowing them to focus on higher-impact coaching and development. Simulation-based training gives agents a safe space to practice complex scenarios, improving readiness before they speak with real customers. We chose Solidroad for the quick integration with our tools and the support from the team during the trial.” #### Result ##### A cultural shift toward data-driven CX Although early in their adoption, Amenitiz is already seeing clear cultural and operational improvements. Bias has been eliminated from quality reviews, thousands of conversations are now assessed instantly, and team leads have reclaimed hours previously lost to manual scoring. Coaching is becoming more data-driven and precise, guided by a complete view of each agent’s performance rather than a small, random sample. Agents themselves have welcomed the shift, knowing they’re now evaluated on the full breadth of their work, not a slice of it. #### Conclusion ##### Transforming CX across the business Solidroad has fundamentally changed how Amenitiz manages quality, coaching, and training. QA is now automated, objective, and scalable. Teams spend more time developing performance and less time on administrative tasks. Coaching is rooted in real, comprehensive data, creating a more fair, transparent, and effective environment for everyone. For Amenitiz, this marks a meaningful step toward a more mature, data-rich CX operation that can continue scaling without compromising quality. Too much time was spent by team leads on QA tasks, and bias impacted results. We weren’t focusing enough on coaching or team performance.” #### More case studies Ryanair validates candidate quality at scale, cutting hiring time in half Tech Mahindra improves NPS by 10 points and quality scores by 5% with consistent quality measurement ## Finom customer story Source: https://solidroad.com/customers/finom ### How Finom Raised the Quality Bar With Solidroad Summarize Title ##### Finom is a fast-growing fintech company helping businesses manage their finances with speed, clarity, and confidence. As the company scaled, maintaining a consistently high bar for customer support quality became more complex. Manual QA workflows limited visibility into real customer experiences and made it difficult to uphold standards without adding headcount. Finom turned to Solidroad to bring structure, consistency, and insight to quality evaluation—without compromising rigor. Today, Solidroad helps the team better understand performance trends, review more conversations, and build a stronger foundation for quality and training as the organization grows. #### The challenge ##### Manual QA limited visibility into customer experience and team performance The Training & Quality (T&Q) team at Finom was under pressure. Their manual evaluation process was: - Time-consuming and resource-heavy , limiting how many conversations could be reviewed - Providing incomplete visibility into agent performance and the overall customer experience - Creating a bottleneck as the company scaled, with no clear path to increasing QA coverage without hiring The workload of our T&Q team was too high. We were spending too much time on manual quality evaluations and couldn’t get a full picture of performance.” Maria Samsonova Training & Quality Lead #### WHY SOLIDROAD? ##### Flexible, AI-driven QA and training built for scale Finom had previously used Klaus (via Zendesk) for manual reviews and explored their AI capabilities, but found the solution not advanced or flexible enough for their needs. Solidroad stood out because of: - A customizable and trainable scorecard system , allowing Finom to define quality in their own terms - Advanced AI feedback and insights , supporting deeper analysis and scalable reviews - A shared platform for both quality evaluation and training , built to connect the two functions seamlessly #### SOLUTION ##### A single platform for quality evaluation, AI insights, and training at scale Finom implemented Solidroad to help redefine and scale their quality function. - The team is currently focused on fine-tuning the scorecard to reflect internal standards - They plan to expand usage into training and AI-driven reporting for performance management - Even in the early stages, Solidroad has enabled them to increase review volume and avoid new hires Solidroad offers a customizable, trainable scorecard and more advanced AI-driven feedback features than what we’d seen elsewhere.” #### RESULT ##### 12% increase in QA coverage and avoided headcount expansion Even before reaching full adoption, Finom has seen clear early impact: - 12% more conversations reviewed - Did not need to hire an additional T&Q specialist , despite team growth - Improved visibility into trends and gaps in team performance - Early steps toward reducing manual workload and increasing training impact #### CONCLUSION ##### A scalable foundation for quality and training in fast-growing teams While still fine-tuning their implementation, Finom sees Solidroad as a long-term enabler of QA and training efficiency. - Supports a scalable QA workflow with AI-backed evaluations - Lays the groundwork for training automation and performance coaching - Provides richer insight into customer experience , helping agents improve continuously We’re now focusing on bringing the scorecard up to our standards. Solidroad helps us spot broader trends in quality across the team.” #### More case studies Ryanair validates candidate quality at scale, cutting hiring time in half Tech Mahindra improves NPS by 10 points and quality scores by 5% with consistent quality measurement ## Crescendo customer story Source: https://solidroad.com/customers/crescendo ### Crescendo standardizes quality validation across global operations Summarize Title ##### Crescendo provides outsourced customer support and back-office operations for startups and tech companies. With teams in Honduras and the Philippines handling diverse client needs - sales, healthcare, tech support, content moderation - Crescendo needed consistent quality validation across all operations. Without standardized assessment, quality varied by region and role, and there was no reliable way to verify that agents met client standards before deployment. By implementing automated quality validation, Crescendo now has consistent measurement across all regions and roles, with 50% less time spent on manual assessment. #### The Challenge ##### Quality assessment wasn't consistent across regions Crescendo's global operations were growing, but quality assessment wasn't standardized. Without consistent validation criteria, quality varied between regions and roles. Manual assessment was subjective and time-consuming, and there was no reliable data to measure whether agents met standards before deployment. Leadership knew quality was uneven but lacked the visibility to pinpoint where. We were stuck using traditional methods that just weren’t keeping up with our growth. On top of that, we lacked real data to measure success, which made it hard to refine our approach and improve training outcomes.” Christina Sadler Director of Training #### Why Solidroad ##### Standardized quality validation that works everywhere Crescendo needed quality validation that worked the same way across all regions and roles. Solidroad offered automated assessment against consistent criteria - language proficiency, communication quality, role-specific competencies - applied uniformly regardless of location. This replaced subjective manual review with objective, data-backed validation. #### Solution ##### Consistent quality standards across all operations Crescendo now uses Solidroad to validate quality across hiring and deployment globally. Candidates complete AI-scored simulations that assess communication quality and role-specific competencies against the same standards, whether they're in Honduras or the Philippines. For deployed agents, ongoing validation ensures quality standards are maintained across all client programs. This creates consistent measurement where there was previously regional variation. We now have consistent quality data across all our global operations. We can validate that agents meet standards before deployment, regardless of region.” #### Result ##### Consistent quality, less manual effort Since implementing standardized quality validation, Crescendo has seen measurable improvements. Time spent on manual assessment dropped by 50%. Applicant pass-through rates increased by 25% as standardized criteria improved hiring consistency. Time to quality threshold decreased by 20% as agents are validated more efficiently. Most importantly, quality measurement is now consistent across all regions. #### Conclusion ##### Quality validation that scales globally Solidroad has given Crescendo what they needed: consistent quality validation across their entire global operation. Every agent - regardless of region or role - is assessed against the same standards. Quality is measured objectively, and validation scales without requiring proportional increases in manual effort. With quality validation proving effective, Crescendo is expanding usage to deeper quality visibility across all client programs. Quality is now consistent across every region. Every agent is validated against the same standards.” #### More case studies Ryanair validates candidate quality at scale, cutting hiring time in half Tech Mahindra improves NPS by 10 points and quality scores by 5% with consistent quality measurement ## AI-Powered Training for Customer Support Agents Source: https://solidroad.com/resources/ai-powered-training-customer-support-agents ### AI-Powered Training for Customer Support Agents: A Complete Guide Summarize Title #### Key Takeaways - The preparedness paradox is the gap between how ready agents feel after onboarding and how hard they find it to apply training to real customer situations. - AI-powered training for support agents means realistic practice scenarios, scored against the same QA criteria used in production, with feedback tied to specific skills. - Generic roleplay does not cover the patterns QA keeps flagging in live conversations, which is where ramp actually slows down. - The score-to-simulation loop turns live QA evidence into targeted practice and then verifies whether behavior changed in later conversations. - Completion rates and quiz scores are weak signals. Live QA score changes on the practiced skill are the honest measure. In our State of CX 2026 report, a survey of 500 full-time support agents, 82.5% said they feel prepared when they start handling real customer interactions. That sounds like a training program working. Then look at what comes next. In the same survey, 53.5% said the hardest part of ramping is applying training to real customer situations. The knowledge transfer worked. The behavior change did not follow. That gap has a name. Solidroad calls it the preparedness paradox. Confidence is high on day one. Performance under live pressure often is not. Most support training does an acceptable job of transferring knowledge and a poor job of changing behavior when the conversation gets messy. This guide is for support leaders who want a better operating model. The premise is simple. AI-powered training works when it trains agents on the conversations they actually get wrong. #### Support training breaks when agents meet real customers Most support training looks successful in onboarding because it measures the wrong things. New hires complete modules, pass quizzes, and rehearse a handful of scripts. By the end of week one, they feel ready. Then a frustrated customer asks for an exception that is not in the playbook, and the gap between knowing and doing shows up. The State of CX 2026 numbers describe that gap clearly. A majority of agents felt prepared before going live. More than half said the hardest part of ramping was applying that training to real customer situations. About 28.9% pointed specifically to not getting enough hands-on practice before they started taking real conversations. A raw cross-tabulation of the same survey data sharpens the point. Among agents who described themselves as very unprepared at the start of their role, 88% cited insufficient hands-on practice as their biggest ramp challenge. Among agents who described themselves as very prepared, that figure was roughly 21%. The four-times gap points to practice fit more than training volume. Static docs and slide decks can transfer knowledge. They do not reliably produce calm, accurate behavior under pressure. Generic roleplay can help newer agents loosen up, but it cannot cover the specific patterns QA keeps flagging in live conversations. And the move toward simulation is already happening. In the same survey, 54.1% of agents reported that customer support simulations are already part of their training. The question for support leaders has moved on: do the simulations match the actual moments where ramp slows down? Training researchers describe this as a transfer problem. A 2011 review on the transfer of training identified realistic training environments, opportunity to perform, and follow-up as factors with strong links to whether training transfers into workplace behavior. That is the real bar for support onboarding: not whether agents finished training, but whether the trained behavior shows up with customers. Training fails when it prepares agents for the idea of support instead of the reality of support. #### AI-powered training means practice built from real support gaps AI-powered training for customer support agents uses AI to generate realistic practice scenarios, score responses against QA criteria, and give feedback tied to the skills agents need to improve. The point is rehearsal of real customer moments at a volume and specificity that managers cannot deliver by hand. One disambiguation matters here. This guide is about training human customer support agents. It is not about training AI customer-service agents that handle conversations on their own. The two markets share vocabulary and confuse buyers. They are different products with different success criteria, and conflating them is one reason “AI in support” feels noisier than it should. Good AI-powered training does four things consistently. It generates scenarios that mirror live customer situations the team actually encounters. It scores agent responses against the same rubric QA uses on real conversations, so practice and production speak the same language. It returns feedback in the moment, while the context of the response is still fresh in the agent’s head. And it adjusts as the agent improves, raising difficulty or shifting persona once a skill is dependable. That last point is the difference between training software and a content library. Static content cannot tell whether the agent got better. AI-powered training can, when it is wired to the same evidence that QA already uses. #### The score-to-simulation loop connects QA to training The score-to-simulation loop is an operating model where every conversation is scored, the highest-value skill gaps are identified, agents practice those gaps in realistic simulations, and live QA data verifies whether behavior improved. It is the named idea this guide is built around. It has four steps. - Score live conversations. Every interaction passes through a consistent rubric instead of a small manual sample. Sampling at low rates leaves most of the evidence on the floor and lets recurring failure patterns hide. - Identify the highest-value skill gaps. Sort the misses by frequency and business impact. A refund handling pattern that erodes margin is a different priority from a tone inconsistency on a low-volume channel. - Turn those gaps into realistic simulations. Build practice that mirrors the moments where agents struggle, with the right persona, channel, language, and difficulty. The further the scenario sits from a real conversation, the less the practice transfers. - Verify whether behavior improved. Check next week’s QA scores on the same skill for the same agents. The scoreboard is live conversations, not the agent’s score on a quiz. The loop is what makes AI useful in support training. QA on its own produces coaching notes that no one practices. Simulations on their own drift toward generic content. Verification is what tells the team whether any of that work changed behavior in production. This is also where the loop closes with the earlier QA coverage argument. Full-coverage QA produces the evidence. Score-to-simulation turns that evidence into behavior change. #### Generic roleplay is not enough Generic roleplay helps newer agents loosen up and rehearse the basics. It does not prepare them for the moments QA flags week after week. Targeted simulation differs from roleplay by drawing scenarios from actual conversation failures and scoring them against the same rubric used in production. The gap shows up in a handful of recurring situations: - Refund pushback when policy and customer expectation collide. - Frustrated customers escalating after a previous bad experience that the current agent did not cause. - Multi-turn troubleshooting where the agent has to reason across messages instead of pattern-matching one screen. - Policy exceptions where the right answer depends on judgment, not lookup. - Regulatory wording that needs to land precisely, in the right place, with no extra words around it. - Escalation decisions where the cost of a wrong call is high and the signal to escalate is subtle. - Recovery after an AI agent handed the customer a wrong answer or a half-finished resolution, where the human agent has to rebuild trust and finish the work without restarting the conversation from scratch. That specificity matters. A 2020 simulation-based learning meta-analysis across 145 empirical studies found a large positive overall effect for simulations in complex-skill learning and pointed to scaffolding as part of what makes simulation work. Customer support is a different domain, but the learning principle maps cleanly: practice needs to resemble the hard part of the job. A new hire can run through ten clean roleplays and still freeze on the first real escalation. The point of targeted practice is the messy, specific moment, repeated until the behavior is dependable in production. #### What AI should personalize in agent training Personalization in AI-powered training means shaping every dimension of practice to match the agent, the team, and the live evidence. Generic scenarios delivered at scale are not personalization. Volume is not the same as fit. When you assess a platform, work through these dimensions: - Scenario source. Are simulations generated from actual conversation performance, or from a generic library the vendor ships with? - Role, persona, channel, and language. Can practice reflect a billing escalation in Spanish over chat, not just a generic English voice call? - Difficulty. Does the system progress from straightforward to high-pressure based on the agent’s recent scores, or is everyone running the same flat track? - Scoring rubric. Are scenarios scored against custom rubrics shaped by your guidelines, SOPs, and knowledge base, or against a default rubric the vendor designed? - Feedback timing. Does the agent see feedback immediately, while the scenario is fresh, or in a digest some hours later? - Manager visibility. Can team leads see who needs targeted practice, on what skill, and how that pattern compares to live QA findings? - QA verification. Does the platform close the loop by checking whether live QA scores on the practiced skill actually improve? AI can generate the practice. Managers still own the judgment. If most of those criteria are missing, the system is roleplay at scale with better production values. That has some value, but it still falls short of what the preparedness paradox needs. #### How to tell whether training changed behavior Completion rates and quiz scores tell you that training happened. They do not tell you that anything changed. Verification means looking at live QA evidence before and after the practice, on the specific skill that was rehearsed. Feedback also has to point agents back to the task, not just score the attempt. Valerie Shute’s Review of Educational Research article on formative feedback defines feedback as information intended to change thinking or behavior, and warns that feedback can backfire when it shifts attention away from the task. That is why QA-linked feedback should name the specific behavior to repeat or change. Signal What it proves What it misses Training completion The agent finished the assigned work Whether the agent can apply the skill with customers Simulation score The agent performed in the practice setting Whether the behavior carries into live conversations Live QA movement The targeted behavior changed in production Whether the improvement came from training alone Stronger signals to track include: - Live QA score changes on the specific skill the agent practiced, week over week. - Fewer repeated errors in the same skill area across consecutive review periods. - Faster, more accurate escalation judgment in messy, multi-turn cases. - Improved compliance language where the wording has to land precisely. - Manager-confirmed coaching progress, with notes that match the QA evidence rather than contradict it. This is also where the preparedness paradox gets resolved honestly. Less prepared agents in the State of CX 2026 survey were more likely to cite lack of hands-on practice and fear of making mistakes. Verification with live QA evidence is what tells you, agent by agent, whether the practice closed those gaps or just made everyone feel busier. A useful instinct here is to treat completion as the floor, not the ceiling. If completion is the only metric, training is a content distribution program. If live QA movement is the metric, training is part of the performance system. #### What Solidroad does differently The Solidroad training platform connects automated QA scoring with training simulations in a single loop, so the practice agents do is built from the conversations they actually struggle with rather than from a generic content shelf. A few practical specifics worth knowing. - Automated QA scoring evaluates 100% of conversations, not a sampled slice, and surfaces risk, compliance gaps, and churn signals as they appear in live data. - Training simulations generate realistic scenarios across personas, channels, difficulty levels, and languages, drawn from the patterns scoring has already flagged. - Simulations are auto-scored against custom QA scorecards shaped by company guidelines, SOPs, and knowledge base content, so practice and production share one rubric. - Feedback goes back to agents immediately, while the response is still fresh and the lesson is concrete. Solidroad has scored more than three million QA conversations to date. That volume gives the scoring side of the loop enough pattern data to inform practice rather than guess at it. Separately, customers using the platform report 33% faster agent ramp, 5x trainer efficiency, and an 80% reduction in time spent building and deploying trainings. The scoring scale and the customer outcomes sit alongside each other rather than in a single causal chain. The scoring volume makes targeted practice possible. The customer outcomes are what teams have observed when they run the loop in production. Solidroad is not the only platform building in this direction. The piece worth evaluating is whether QA scoring and training simulations share the same rubric and the same evidence trail, so that practice and live work measure the same thing. #### Train the gaps, not the general case AI-powered training matters when it changes how agents behave in live conversations, not when it produces more training content. The operating model is straightforward. Score what happens. Identify the gaps that actually cost the business. Simulate the exact moments agents mishandle. Verify in the following weeks of QA whether anything moved. Confidence on day one is easy. Behavior under pressure is the bar that matters, and the only honest way to know whether training is working is to read it back from the conversations themselves. Train the gaps, not the general case. #### More articles Announcements Sep 14, 2026 ###### Solidroad and Customer Intelligence Platform Unwrap Partner to Turn Insight Into Action Jul 29, 2026 ###### Solidroad Named OpenAI Select Partner Quality Assurance Jul 10, 2026 ###### How to QA AI Customer Service Agents ## Automating QA for Banking Contact Centers Source: https://solidroad.com/resources/automate-qa-banking-contact-centers ### How to Automate QA for Banking Contact Centers Summarize Title #### Key Takeaways - Automated QA scores every banking conversation for risk instead of grading a small random slice. - Humans review flagged conversations, not every conversation, so attention goes to fraud, disputes, complaints, vulnerable customers, and high-value coaching moments. - Routing should prioritize complaints, EFT disputes, authentication failures, UDAAP and fair-treatment risk, fraud and scam signals, vulnerable-customer moments, and chatbot handoff failures. - Random sampling still belongs in the model as a calibration and governance check. - Findings create value only when they feed complaint handling, compliance follow-up, fraud escalation, process fixes, and coaching. To automate QA for banking contact centers, score every conversation for complaints, EFT disputes, authentication risk, fraud signals, vulnerable-customer moments, and chatbot handoff failures, then route the highest-risk interactions to the right human owner. A banking contact center handles thousands of routine conversations a day. Most are balance checks, card activations, payment confirmations, and address changes. A few are not. One caller is reporting an unauthorized electronic fund transfer. Another fails authentication twice, then asks to change a phone number and move money. A third never says “complaint,” but insists the bank charged a fee it promised to waive. A fourth is stuck in a chatbot loop while trying to access an account to pay rent. A fifth discloses bereavement and does not understand the options in front of her. Those conversations carry the risks that define banking support: fraud and scam exposure, EFT disputes, authentication failures, UDAAP and fair-treatment problems, missed complaints, chatbot handoff failures, and vulnerable customers who need more care than a script provides. Random QA sampling will almost never catch every one of those moments. In our State of CX 2026 report, a survey of 500 customer support agents, 81% said most conversations are never reviewed. When a bank reviews a small percentage of interactions at random, the rare high-impact conversation is often the one it misses. The fix is not to sample faster. It is to score every conversation for risk, then route the few that matter to the right human. Full coverage is the input. Compliance-aware risk routing is the operating model. #### Why random QA sampling breaks in banking contact centers Random QA sampling breaks in banking contact centers because the highest-risk conversations are rare, high-impact, and easy to miss in a small sample. A thousand routine balance questions can pass through review untouched and nothing is lost. One missed unauthorized-transfer report, one unrecognized complaint, or one account-takeover signal can leave customer harm unresolved or hide a pattern managers need to investigate. Sampling treats every conversation as if it has the same QA value. Banking operations cannot afford that assumption. The problem grows as service moves into automated channels. The CFPB’s research on chatbots in consumer finance notes that financial institutions increasingly rely on chatbots, and that poorly deployed bots can give inaccurate information, fail to recognize disputes, and create privacy and trust problems. A random sample of human calls says little about what happened in those bot sessions or at the handoff to an agent. Banking support is also a regulated relationship, not generic ticket handling. The CFPB’s request for information on relationship banking and customer service frames timely account information, responsiveness, and care as core expectations. A sampling rate built for convenience does not give a QA team enough signal to see whether those expectations are being met across phone, chat, email, app, and automated channels. Sampling gives a defensible workload. It does not give a dependable view of banking risk. #### What automated QA should cover in banking contact centers Automated QA should cover every banking conversation against risk categories that reflect customer harm, regulatory exposure, fraud pressure, and coaching value. A scoring rubric built only on tone and resolution metrics will miss most of what distinguishes banking contact-center risk from general support quality. The scorecard needs to reflect moments where a missed signal changes what the bank should do next. The table below maps common signal types to example triggers and likely routing owners. Owner names vary by bank, but the routing principle should be explicit. Risk category Example trigger Likely human owner Authentication and account access Caller fails identity checks, then requests a phone-number change or funds transfer Fraud, risk operations, or account-access team EFT disputes and payment errors Customer reports an unauthorized debit-card charge, missed transfer, or incorrect payment Disputes, payments operations, or complaints team Complaint recognition and escalation Customer says they were misled, treated unfairly, or left with unresolved harm Complaints or compliance operations UDAAP and fair-treatment risk Agent gives an unclear fee explanation or inconsistent product terms Compliance, QA, or policy owner Fraud, scam, and account-takeover signals Customer describes suspicious payment instructions, coercion, phishing, or unusual account access Fraud operations Vulnerable customers and hardship cues Customer discloses bereavement, disability, financial distress, confusion, or coercion Specialist support, QA, or complaints Chatbot and human handoff failures Bot loops a customer with an urgent account or dispute issue Digital support, QA, or channel owner Process and policy breakdowns Multiple agents give different answers about overdrafts, holds, funds availability, or documentation Operations, policy, or knowledge-base owner The CFPB’s Electronic Fund Transfers FAQ gives a clear example of why payment-dispute detection matters at the conversation level. The CFPB lists examples of EFT errors, including unauthorized EFTs, incorrect EFTs, omitted EFTs, bookkeeping errors, and requests for documentation or clarification. A customer does not need to use perfect internal language for the conversation to matter. The QA system should be able to spot the signal. Authentication deserves the same treatment. FFIEC guidance on authentication and access covers customer call centers, high-risk transactions, unauthorized access, layered security, monitoring, logging, and reporting. Automated QA does not own authentication policy. It can surface where the contact center is seeing risk signals that fraud and risk teams need to review. #### Which banking conversations should trigger human review Banking conversations should trigger human review when automated QA finds potential customer harm, compliance exposure, fraud risk, vulnerability, repeated process failure, or high coaching value. The goal is to move reviewer attention away from clean, routine calls and toward the interactions where judgment, escalation, coaching, or a process fix can change the next outcome. A customer reporting an unauthorized debit-card transaction should reach a disputes or payments owner, not sit in a generic low-resolution bucket. A fee or disclosure dispute should reach someone who can judge whether the explanation was accurate, because the FDIC’s UDAAP examination material connects misleading explanations and customer harm to fair-treatment risk. A caller who fails authentication and then tries to change contact details or move money should route for fraud or account-takeover review. The same routing logic applies outside classic voice calls. An urgent chatbot loop, where a customer cannot reach a person while a real need goes unmet, should route for both CX and escalation review. Disclosures of bereavement, hardship, disability, coercion, or confusion should route to specialists. When several agents give inconsistent answers about overdrafts, funds availability, or dispute documentation, the pattern should route to process owners before it becomes another individual coaching note. One principle keeps this honest. A flag is a routing signal, not a finding. The system says this conversation deserves a look. A person decides whether anything is actually wrong. #### What to automate and what humans still own Banking QA teams should automate the visibility layer while humans own interpretation, calibration, customer-impact decisions, and corrective action. That split keeps automation focused on coverage and routing, and keeps judgment with the people accountable for complaints, fraud, compliance, and coaching. Automate Humans own Conversation scoring across every channel Complaint classification under bank policy Risk-category detection Fraud and account-takeover decisions Keyword and intent signals Compliance interpretation Queue routing to the right owner Customer remediation choices Trend surfacing across products and agents Vulnerable-customer support decisions Calibration sample selection Policy and process fixes Recurring issue dashboards Agent coaching and scorecard calibration Random sampling has a place on the automated side and the human side at once. The model can pull calibration samples, and reviewers can use those samples to test whether the scoring and risk rules are still accurate. If automated QA starts missing complaints or over-flagging clean calls, the calibration sample is how the team catches that drift before it skews the whole queue. #### How QA findings feed complaints, compliance, coaching, and process fixes Banking automated QA creates value only when findings move into complaint handling, compliance follow-up, coaching, fraud escalation, or process fixes. A score sitting in a dashboard changes nothing. The work is in routing and follow-through. The OCC’s Compliance Management Systems handbook describes a complaint process that defines complaints and risk levels, assigns owners, escalates significant cases, tracks responses, analyzes root causes and trends, and considers complaints from all channels. Automated QA can feed that process because it can recognize a possible complaint a customer never labeled and surface patterns across phone, chat, and bot transcripts. The CFPB’s Consumer Complaint Program reviews complaint cohorts for accuracy, timeliness, completeness, and anomalies, and uses text analytics to identify trends. A bank can use the same discipline internally on its own conversations. Good follow-through separates the problem types. An agent who missed a cue needs coaching. A policy that confuses customers needs a policy fix. A knowledge-base issue that produces inconsistent answers needs a content owner. A repeated fraud pattern needs fraud operations. When a recurring skill gap shows up, findings can feed targeted practice through a score-to-simulation loop, so coaching draws on real conversations rather than generic samples. The last step closes the loop: check whether the next batch of conversations improves. #### What Solidroad does differently Solidroad turns the banking QA operating model into a workflow by scoring 100% of conversations, surfacing risk and compliance gaps, and connecting repeated skill gaps to coaching and training simulations. The product point is not that a bank can automate judgment. The product point is that full conversation coverage gives human owners a better starting point. Solidroad’s automated QA scoring evaluates every interaction across live chat, video, email, phone, and multiple languages, and has scored more than 3 million QA conversations to date. Across its customer base, Solidroad reports a 20x increase in QA coverage, a 90% reduction in QA time per interaction, and up to 10x analyst throughput. For banking contact centers, that channel coverage matters because risk does not stay in one queue. A complaint can begin in chat. An authentication risk can appear on a phone call. A chatbot can miss a dispute signal before any human reviews the case. Teams set what to look for with custom scorecards, so banking risk categories like authentication, EFT disputes, complaint cues, vulnerable-customer support, fraud escalation, accuracy, and fair-treatment language become explicit evaluation criteria. The scoring surfaces risk, compliance gaps, coaching opportunities, churn signals, and process gaps, and recurring patterns can roll up into process-improvement views that separate agent issues from policy and knowledge-base issues. The follow-through connects to the same data. Repeated skill gaps can feed agent coaching built on real conversations and training simulations for targeted practice. Teams that need it can review the platform’s security posture as part of their own governance. Solidroad supports the workflow; it does not replace a bank’s compliance program. #### Better banking QA starts with risk-based visibility Better banking QA starts with seeing every conversation, routing the few that carry the most risk, and using the findings to improve service quality. Full coverage is not the finish line. It is the data layer that makes targeted human judgment possible at scale. The operating model is straightforward to state and worth building deliberately. - Define banking QA risk categories, from authentication and EFT disputes to complaints, fair treatment, fraud, vulnerable customers, and chatbot failures. - Score every conversation across every channel. - Route flagged conversations by risk and learning value to the owner who can act. - Keep human review for judgment, customer-impact decisions, and calibration. - Act through coaching, process fixes, complaint follow-up, and fraud or compliance escalation. - Check the next batch of conversations to see whether the fixes worked. That is the shift from random sampling to compliance-aware risk routing. Banking contact centers do not need more low-value review. They need a way to see the full conversation set, send attention to the interactions that matter, and close the loop when the same issue shows up again. #### More articles Announcements Sep 14, 2026 ###### Solidroad and Customer Intelligence Platform Unwrap Partner to Turn Insight Into Action Jul 29, 2026 ###### Solidroad Named OpenAI Select Partner Quality Assurance Jul 10, 2026 ###### How to QA AI Customer Service Agents ## Automating QA for Insurance Contact Centers Source: https://solidroad.com/resources/automate-qa-insurance-contact-centers ### How to Automate QA for Insurance Contact Centers Summarize Title #### Key Takeaways - 100% conversation coverage is the input to insurance QA, not the end state. Risk-based routing is the operating model. - Automated QA should score claims, coverage explanations, complaints, vulnerability signals, fraud and SIU cues, privacy handling, documentation quality, intermediary handoffs, and AI-agent answers. - Humans still own claim interpretation, complaint decisions, fraud referrals, vulnerable-customer support, coaching, calibration, and process fixes. - Random sampling keeps a role in calibration and governance, but it is too weak to be the primary way an insurer finds claims and conduct risk. Insurance contact centers should automate QA by scoring every conversation for claims and conduct risk, then routing the conversations that actually need human judgment. The interactions that matter most in insurance are not spread evenly across the queue. They sit inside claim status calls, coverage explanations, disputed settlements, complaint language, vulnerable-customer disclosures, fraud and SIU cues, documentation gaps, and intermediary handoffs where a broker or TPA says one thing and the insurer says another. Automated QA in insurance is not about reviewing the same random sample faster. It is about catching the claim, complaint, coverage, vulnerability, fraud, and documentation risks a random sample misses without pretending a machine can settle a claim. Every conversation gets scored against defined risk categories. The highest-risk and highest-learning-value conversations then get routed to the people who can act on them: claims QA, complaint handling, SIU and fraud teams, vulnerable-customer specialists, and process owners. Coverage is the input. Risk-based routing is the operating model. The goal is to stop treating visibility as luck, so a missed coverage limit, an unrecognized complaint, or a bereaved customer who cannot finish an online claim form does not disappear into the pile of conversations nobody reviewed. #### Why random QA sampling breaks in insurance contact centers Random QA sampling breaks in insurance contact centers because the highest-risk conversations are rare, cluster around claim and conduct events, and rarely surface in a small random pull. A monthly sample can miss the coverage explanation, complaint signal, vulnerable-customer disclosure, fraud cue, or documentation error that should have triggered human review the day it happened. Random sampling assumes risk is spread evenly enough that a small review set can stand in for the whole queue. Insurance support does not behave that way. Routine policy servicing can quickly give way to a weather event, total-loss spike, repair-network delay, or wave of customers asking the same coverage question. In our State of CX 2026 report - a survey of 500 customer support agents - we found that 81% of agents say most conversations are never reviewed. In an insurance setting, that unseen volume is not just a quality blind spot. It is claim files, complaint files, and audit trails that nobody scored. The NAIC’s Unfair Claims Settlement Practices Act is model language rather than a single national rule, but it is a useful US reference point for QA categories such as explaining policy provisions, acknowledging claim communications, applying reasonable investigation standards, and explaining denials. Jurisdiction-specific rules vary by state, country, and line of business, so this article uses US, UK, and EU sources as context, not as one combined compliance standard or legal checklist. Random samples can still test calibration. They should no longer be the main way an insurer finds claims and conduct risk. #### What automated QA should score in insurance conversations Automated QA for insurance conversations should score the signals that create claims and conduct risk, not just tone or script adherence. Useful categories include coverage accuracy, claim acknowledgement, complaint recognition, vulnerability signals, fraud and SIU referral cues, privacy handling, documentation quality, intermediary handoff consistency, and AI-agent answer accuracy. Insurance scorecards need to reflect the work insurers actually do. A generic support rubric asks whether the agent greeted the customer, sounded empathetic, and resolved the issue. Those criteria do not cover claim intake, a disputed settlement, a customer who cannot complete a digital claim form, or an agent who misstates an exclusion. The scorecard should create routing signals, not just a quality score. QA category What to score Human owner Coverage explanations Policy wording accuracy, exclusions, limits, deductibles, and required documentation Claims QA or senior claim handler Claim acknowledgement Status accuracy, next steps, response completeness, and timeline explanation Claims operations Complaints Complaint language, dissatisfaction, delay frustration, and escalation requests Complaint handling or compliance Vulnerable customers Bereavement, disability, financial distress, trauma, low digital confidence, and access needs Specialist support or team lead Fraud and SIU cues Inconsistent loss details, suspicious third-party involvement, and identity theft signals SIU or fraud team Privacy and authorization Authentication, third-party permission, claim-file access, and nonpublic information handling Compliance or security Documentation quality File notes, document requests, duplicate requests, and handoff notes Intermediary handoffs Broker, TPA, repairer, adjuster, or outsourced-provider consistency Operations owner AI-agent answers Unsupported coverage statements, invented process steps, and missed escalation cues AI QA owner or QA lead The scorecard is not a legal test. Claims timelines, complaint rules, and disclosure requirements differ across lines and jurisdictions. What the scorecard gives claims, compliance, and operations leaders is a shared way to find the conversations that deserve a closer look. #### Which insurance conversations should trigger human review Insurance conversations should trigger human review when they include a claim denial, settlement dispute, coverage uncertainty, complaint language, vulnerable-customer signal, fraud or SIU cue, repeated documentation friction, AI-agent uncertainty, or conflicting information from an intermediary. Automated QA should route those conversations by risk and by owner, not hold them for the next scheduled sample. Some triggers are loud. A policyholder asks whether temporary accommodation is covered after a flood. An incomplete or incorrect answer may shape the customer’s understanding of a live claim. A claimant disputes a total-loss vehicle valuation and wants to know the basis for the figure. A customer says, “I want to complain.” A caller changes key loss details twice in the same conversation. None of these should wait for a random pull weeks later. Other triggers are quiet, which is why they get missed. A customer says they cannot complete an online claim form because of disability, bereavement, low digital confidence, or distress after a loss. A claim handler requests the same documents again without explaining why. This pattern can signal a process issue, not only an agent skill gap. A broker gives one answer and the insurer gives another, leaving the customer caught between conflicting explanations. An AI agent responds with confident coverage wording the policy does not support. The FCA’s review of support for struggling customers is UK-specific, but it offers a useful conduct-risk reference point. The FCA found some home and motor insurers needed to improve vulnerable-customer treatment, claims handling, outcome monitoring, and information sharing where intermediaries were involved in claims settlement. Read that as an example of the risks worth scoring, not as a rule for every insurer. Routing matters as much as detection. Coverage uncertainty belongs with claims QA or a senior handler. Complaint language belongs with complaint handling. Fraud cues belong with SIU. Vulnerability signals belong with trained support owners, not a generic quality queue. #### What to automate and what humans still own Insurance QA automation should score, group, and route conversations, while humans retain judgment over claim interpretation, complaint classification, fraud referral decisions, vulnerable-customer support, coaching, calibration, and process fixes. Automation creates visibility. Human reviewers decide what that visibility means and what action follows. Automated QA does the high-volume work manual QA cannot scale. It applies defined scorecards to every conversation, flags likely risk categories, groups repeated issues, routes cases to the right owner, and shows whether a pattern is growing or shrinking. Automated QA can also hold calibration samples so QA leaders can test whether the scoring model still tracks human judgment. Human reviewers own the decisions with customer, claim, or conduct consequences. A flagged coverage conversation needs a qualified person to read the policy and decide the next action. A complaint signal needs complaint-process ownership. A fraud or SIU cue needs an authorized investigator to decide whether referral is appropriate. A vulnerability signal needs a trained person, not a score, to respond to the customer. Automated QA should never determine coverage, approve or deny a claim, settle a claim, or make a fraud determination on its own. The NAIC’s AI model bulletin is a useful reference when an insurer uses AI systems in claim administration, case management, payment, or fraud detection. The bulletin points toward governance, risk controls, documentation, testing, and human oversight proportionate to consumer risk. Treat that as governance context for AI use, not a universal rule for every contact center tool. The boundary should be explicit. Automated QA flags potential risk. Humans decide claim outcomes, complaint outcomes, fraud referrals, coaching plans, and process changes. Random sampling stays in the picture as a calibration check on both the model and the reviewers. #### How QA findings feed claims, compliance, and coaching action Insurance QA findings create value when they become claims, compliance, and coaching action. Repeated coverage confusion may call for policy wording guidance. Repeated document requests may point to a broken process. Repeated complaint misses may call for escalation training and a complaint-handling review. A finding that changes nothing is just a logged observation. The action loop works best when it separates five kinds of findings. - Agent issues, such as a weak explanation, missed empathy, or poor next-step framing. - Process issues, such as duplicate document requests or an unclear claim-status workflow. - Policy wording issues, such as recurring customer confusion about exclusions, limits, deductibles, or temporary accommodation. - Handoff issues, such as a broker, TPA, repairer, or adjuster giving the customer a different answer than the insurer. - Training issues, such as agents missing complaint language or failing to recognize vulnerability signals. This separation matters because the fix differs in each case. Coaching helps when one agent misses a known standard. Process work helps when many agents repeat the same friction. Policy guidance helps when customers keep asking the same coverage question. Intermediary governance helps when the customer hears conflicting answers from different parties. EIOPA’s complaints-handling guidelines are EU guidance rather than a global rulebook, but they offer useful logic: connect complaints to internal follow-up and root-cause analysis of recurring issues. The point is operational, not academic. QA leaders should not stop at “we found the issue.” The loop closes only when later conversations show fewer misses. #### How Solidroad supports risk-based insurance QA Solidroad supports risk-based insurance QA by scoring 100% of support conversations, applying custom scorecards, surfacing risk and compliance gaps, and connecting recurring findings to coaching and training simulations. The product is a practical way to run the operating model, not the reason the operating model exists. Insurance teams can use Solidroad’s automated QA scoring to evaluate conversations across live chat, video, email, phone, and multiple languages. Teams can build custom scorecards for insurance-specific criteria such as coverage explanation quality, complaint recognition, vulnerable-customer support, documentation quality, privacy handling, AI-agent answers, and intermediary handoff consistency. The useful part is what happens after scoring. A risk or compliance signal can be routed to a human reviewer. A recurring pattern can feed process improvements. A coaching need can feed agent coaching or a training simulation built from the conversation type agents are actually mishandling, so the fix reaches the agent rather than sitting in a report. Solidroad has scored more than 3 million QA conversations. Approved proof points include a 20x increase in QA coverage, a 90% reduction in QA time per interaction, and 10x analyst throughput. For security-sensitive teams, Solidroad is also SOC 2 Type 2 certified and ISO 27001 certified. The product does not move the human boundary. Solidroad helps teams see and route risk faster. Insurance leaders still define the scorecards, name the escalation owners, run the calibration process, and apply the jurisdiction-specific controls that fit their lines of business. #### Better insurance QA means fewer unseen risks Better insurance QA means fewer unseen risks because every conversation becomes visible, scored, and routed by risk or learning value. The goal is not more manual review. The goal is a repeatable loop: score, prioritize, review, coach, fix the process, and verify the next batch. Insurance contact centers do not need a bigger random sample as much as they need a better way to decide where human attention goes. A 100% scoring layer gives leaders the coverage. Claims-risk and conduct-risk routing turns that coverage into action while random sampling stays in calibration and governance. The stronger model is simple to state and harder to skip: score every conversation, route the ones that need judgment, keep humans in control of claim and conduct decisions, and keep checking whether the next conversations improve. #### More articles Announcements Sep 14, 2026 ###### Solidroad and Customer Intelligence Platform Unwrap Partner to Turn Insight Into Action Jul 29, 2026 ###### Solidroad Named OpenAI Select Partner Quality Assurance Jul 10, 2026 ###### How to QA AI Customer Service Agents ## Automating QA for BPO Operations Source: https://solidroad.com/resources/automate-qa-bpo-operations ### How to Automate QA for BPO Operations Summarize Title To automate QA for BPO operations, score every conversation across every client program, then route human review by account risk, SLA risk, compliance risk, and coaching value. A BPO does not manage one queue with one rubric. It manages multi-client operations with account-specific scorecards, SLAs, escalation paths, calibration routines, and client governance expectations across multiple sites, shifts, and languages. The gap is not volume alone. It is fragmentation. One agent pool may support an e-commerce program, a fintech program, and a travel program in the same week, and each program defines a good conversation differently. Multi-site delivery adds another layer because the same scorecard can perform differently across an onshore team, a nearshore pod, and an offshore night shift. Calibration keeps QA analysts, team leads, trainers, and client-side reviewers working from the same standard, but calibration only covers the conversations someone actually checks. The goal is not to grade the same random sample faster. The goal is to see every conversation across every account, then send the conversations that carry real risk or learning value to the right human owner. Coverage creates visibility. Account-aware risk routing turns that visibility into an operating model. #### Random sampling hides risk across BPO accounts Random sampling hides risk across BPO accounts because account averages can miss the site, queue, shift, language, or client-specific SOP where quality is drifting. In outsourced CX, fragmentation is as much of a problem as volume. Sampling made sense when QA was manual. An analyst could review only a fixed number of conversations per agent each month, so teams sampled a few interactions and treated the score as a proxy for the rest. That was a workload constraint, not a quality philosophy. BPO operations stretch that thin sample past its breaking point. A single-brand support team has one rubric and one definition of good. A BPO has many. A two-conversation sample per agent can show acceptable averages while a Spanish-language queue misses an identity-verification step or a night-shift team skips a refund escalation for one client. The average looks fine. The client escalation does not. The coverage gap is measurable. In our State of CX 2026 report - a survey of 500 customer support agents - we found that 81% of agents said most conversations are never reviewed, and only 37.4% said even 10% of interactions get looked at. For a BPO running different client programs side by side, the unreviewed majority is where account-specific risk can build. ISO 18295-1 covers customer contact center service requirements across in-house and outsourced operations, sectors, and channels. The standard does not require a specific technology. It is useful here because it shows how broad the quality surface is in outsourced contact center work. #### Automated QA should score every conversation against the right client standard Automated QA should score every BPO conversation against the right client standard, not one generic scorecard. Each client program needs account-specific criteria for SOPs, scripts, escalation paths, SLAs, regulated language, brand tone, and channel rules. ##### Client-specific scorecards are the starting point Client-specific scorecards are the starting point because a BPO has no single quality definition to automate against. An e-commerce program may grade refund-policy accuracy and tone. A fintech program may grade identity verification and disclosure language. A SaaS program may grade escalation accuracy and technical clarity. One universal rubric flattens those differences. The same agent behavior can be acceptable on one account and risky on another. The practical move is to encode each client’s approved workflow as its own scorecard: required openings, escalation triggers, prohibited phrases, SLA checkpoints, and channel-specific rules. COPC describes its CX Standard as a performance management system for customer-experience operations, digital assisted channels, employee engagement, and metrics. That framing fits BPO QA because quality has to be measured across channels and work types, not inside one contact type. ##### Full coverage has to work across sites, channels, and languages Full coverage has to work across sites, channels, and languages because BPO risk does not stay inside one location or one medium. A program might run live chat onshore, phone support offshore, and email across both, with a separate Spanish-language queue handled by a different pod. That is where full coverage matters. Two queues can pass the same account scorecard on average while the Spanish-language queue skips identity verification and the English-language queue completes it consistently. Random sampling may miss the pattern. Every-conversation scoring can surface it early and route the issue to the account manager, QA lead, and site lead. ##### Account dashboards should separate agent, process, and policy issues Account dashboards should separate agent gaps from process gaps and policy gaps so the right owner sees the right pattern. A low score from one agent needs coaching. A low score that appears across an entire shift may mean an SOP, staffing, or workflow issue. A low score tied to missing required language is a policy problem. Useful dashboard cuts include client, queue, channel, site, team, language, issue type, SLA status, and scorecard item. When a client asks why quality dipped, the account lead needs the pattern, owner, and action, not only the average score. #### The right conversations should trigger human review The right BPO conversations for human review are the ones with client risk, SLA risk, compliance risk, coaching value, or repeated process failure. Full coverage does not mean humans review every interaction. It means humans start with the conversations where judgment changes the outcome. ##### Route by client risk, not only by low score Route by client risk, not only by low score, because the most important conversation in a BPO is often not the lowest-scoring one. A friendly, high-scoring call that skips a contractual escalation can matter more than a brusque call that resolved correctly. Routing rules should reflect account consequence, launch stage, SLA exposure, and client commitments. In the first two weeks of a new client program, full coverage can route a launch window to QA so SOP gaps appear before they become client escalations. ##### Route SLA and escalation failures to account owners Route SLA and escalation failures to account owners because those patterns become client-governance problems quickly. A conversation that meets the scorecard but misses a required callback window or drops an escalation to the wrong tier carries relationship risk that an agent-coaching queue alone cannot address. When full coverage shows a repeated failed-handoff pattern on one program, the account owner needs the affected conversations, the team involved, the client commitment, and the trend. That is the evidence needed for the next review. ##### Route sensitive data and disclosure issues to specialist reviewers Route sensitive data and disclosure issues to specialist reviewers because these risks vary by account and need trained judgment. Public sources can guide risk categories without turning QA into a compliance manual. A payment-support account may need flags for card-data handling on recorded calls. An outbound retention account may need checks for required opening language. An account handling EU personal data may need QA fields tied to documented client instructions. These examples do not make automated QA a compliance solution. They show why account scorecards and routing destinations differ. #### Humans still own calibration, coaching, and client governance Humans still own BPO QA calibration, coaching judgment, client governance, and process fixes because automated scoring creates signals, not accountability. QA leaders decide whether a pattern is an agent gap, a rubric gap, a workflow gap, a policy gap, or a client-process issue. ##### Keep random sampling for calibration Keep random sampling for calibration even after a program moves to full coverage because the thing being tested changes. Sampling is no longer how a BPO covers conversations. It becomes how a BPO checks its scoring. ICMI describes calibration sessions as bringing contact center stakeholders together to review contacts for consistent scoring and fairness. That work still matters when two analysts disagree on whether a conversation met the empathy criterion or whether an escalation path applied to a specific client scenario. ##### Use QA findings in client governance reviews Use QA findings in client governance reviews because a BPO’s QA program has two audiences: internal operations and the client. ISO 18295-2 covers organizations using contact center services and the arrangements needed to meet customer expectations. Full-coverage QA gives the account team defensible reporting for those reviews instead of a sampled score the client can reasonably question. A stronger governance view shows top risk categories by account, trend movement by site and channel, high-volume scorecard misses, and actions taken. It also gives the client a clean path to clarify policy when a help-center article, SOP, or escalation rule is creating quality drift. ##### Turn repeated gaps into coaching and process fixes Turn repeated gaps into coaching and process fixes because a signal nobody acts on is just a larger dashboard. An agent who repeatedly misses an escalation trigger needs coaching. A team that misses the same SOP step needs retraining or a workflow change. A pattern across agents, sites, and accounts may point to process or policy design. USAGov’s Public Experience Contact Center describes QA work that includes live monitoring, recorded call and chat analysis, SLAs, KPIs, customer feedback, and continuous improvement with a contractor. NIST AI RMF Core frames AI risk management around govern, map, measure, and manage. Together, those sources support the same operating point: automation measures more work, but humans still own the decisions, documentation, and follow-through. #### QA findings should prove what changed next BPO QA findings should prove what changed next by connecting each recurring issue to an owner, a coaching or process improvement action, and follow-up data from the next batch of conversations. Without the follow-up step, full coverage becomes a larger dashboard. The working model is score, prioritize, coach, verify. Score every conversation across the account. Prioritize the patterns that carry client, SLA, or compliance consequence. Assign each one an owner and an action. Then review the next batch of full-coverage conversations to see whether the pattern moved. That last step is what makes QA findings client-ready. When a client asks what happened with the escalation failures from three weeks ago, the answer should include what changed, who acted, and what the next batch showed. Pattern, owner, action, evidence. The score-to-simulation loop fits here as follow-through. When a recurring gap is really a skill gap, a QA finding can feed training simulations rather than another line in a coaching log. That connection stays downstream of the QA operating model. #### How Solidroad supports full-coverage QA for BPO teams Solidroad supports full-coverage QA for BPO teams by scoring 100% of conversations, applying custom scorecards, surfacing risk and coaching signals, and helping teams connect QA findings to targeted training. The BPO value is consistent visibility across client programs, sites, and languages. Solidroad’s automated QA scoring evaluates support conversations across live chat, video, email, phone, and multiple languages. BPO teams can use custom scorecards for account-specific standards, then connect findings to coaching, QA, account governance, and targeted practice. Solidroad has scored more than 3 million QA conversations and reports proof points including a 20x increase in QA coverage, a 90% reduction in QA time per interaction, and 10x analyst throughput. Tech Mahindra, listed as an Information Technology and Business Process Outsourcing organization, needed better visibility into agent quality across different lines of business, and Solidroad contributed to a 10-point NPS increase and a 5% improvement in quality scores. That is contribution language, not a sole-cause claim. For data handling and governance questions, route readers to Solidroad’s security page instead of implying a regulatory result. #### Build QA automation around account-aware risk routing BPO teams should build QA automation around account-aware risk routing because coverage only matters when it changes where human attention goes. The end state is a loop: score every conversation, route the riskiest cases, act on the pattern, and verify whether the next batch improves. The practical starting sequence is concrete. Define an account-specific scorecard for each client program, including SOPs, scripts, SLAs, and regulated language. Choose routing triggers for SOP deviation, SLA failure, disclosure gap, sensitive data handling, escalation miss, and repeated coaching pattern. Assign each trigger to the right owner, whether that is a QA analyst, team lead, trainer, compliance reviewer, account manager, or operations lead. Keep a random sample running for calibration so the scoring itself stays honest. Act on patterns that repeat. Read the next batch to confirm whether they moved. That is the difference between a bigger dashboard and a better operating model. Full coverage gives a BPO the visibility a random sample cannot. Account-aware risk routing turns that visibility into client governance, calibration, coaching, and process action across every program the BPO runs. #### More articles Announcements Sep 14, 2026 ###### Solidroad and Customer Intelligence Platform Unwrap Partner to Turn Insight Into Action Jul 29, 2026 ###### Solidroad Named OpenAI Select Partner Quality Assurance Jul 10, 2026 ###### How to QA AI Customer Service Agents ## Contact Center QA Software for Fintech Source: https://solidroad.com/resources/contact-center-qa-software-fintech ### The 10 Best Contact Center QA Software for Fintech Teams (2026) Summarize Title #### Key Takeaways - QA teams leave 81% of conversations unreviewed, according to our State of CX 2026 survey of 500 agents. In fintech, those unreviewed conversations are where regulatory exposure concentrates. - The best fintech QA software includes 100% conversation coverage, real-time compliance monitoring, AI agent QA, QA-to-training integration, and security certifications for financial data handling. - AI agents create a new compliance risk for fintech contact centers: incorrect responses, hallucinated product details, and non-compliant advice at scale. - Solidroad leads fintech QA because it scores 100% of conversations and creates documented audit trails for regulatory inquiries - no other tool on this list integrates QA findings with personalized agent training. In our State of CX 2026 report - a survey of 500 customer support agents - we found that 81% of support conversations are never reviewed. In fintech, unreviewed conversations are unmonitored exposure, where potential UDAAP violations and FCA Consumer Duty breaches could be going undetected until they’re found by a regulator. The reason 81% of conversations go unreviewed is simple: Manual QA doesn’t scale. Most teams sample 1-5% of interactions and extrapolate. The right QA software closes that gap, moving fintech teams from a 1-5% sample to 100% coverage. Below, you’ll find 10 contact center QA tools compared across five fintech-specific dimensions: conversation coverage as regulatory audit infrastructure, compliance monitoring capabilities, AI agent QA support, QA-to-training integration, and security certifications for financial data handling. This list covers QA tools for teams across retail banking, fintech lending, credit cards, payments, and insurance. Solidroad is our platform, and it appears first on this list. We’ve included honest limitations alongside strengths for every tool. #### Fintech contact center QA software at a glance The table below compares 10 contact center QA tools on five dimensions fintech compliance teams actually need - conversation coverage, compliance monitoring, AI agent QA, QA-to-training integration, and security certifications. #### How to evaluate contact center QA software for fintech Most QA software evaluations focus on scorecard flexibility, ease of use, and integrations. For fintech teams, those are table stakes. The criteria that determine regulatory audit readiness go further - and the five we use to evaluate each tool in this comparison reflect what fintech compliance teams actually need. ##### Conversation coverage and audit readiness Fintech teams can’t afford to discover compliance violations when a regulator does. 100% conversation coverage means knowing what’s in every interaction before it becomes an enforcement problem. Look for platforms that score every conversation automatically and create searchable audit trails - not just those that record calls for later manual review. “We reviewed 100% of interactions and scored them against compliance rubrics” is a defensible position. “We randomly sampled 2% of calls” is not. ##### Regulatory compliance monitoring Contact center QA software for fintech must scan every customer interaction for signs of regulatory violations in real time - flagging missing UDAAP disclosures, FCA Consumer Duty failures, and AML red flags as they occur, not after a complaint surfaces. Generic “compliance monitoring” features aren’t sufficient when regulators like the FCA can levy fines based on firm size for consumer harm. For instance, HSBC’s £6.2 million fine and £185 million in customer redress came from violation patterns that sampling-based QA missed entirely. QA software that flags regulatory risks in real time - during or immediately after an interaction - prevents that accumulation before it becomes an action. ##### AI agent QA and hallucination detection As fintech companies deploy AI agents for customer interactions, QA software must monitor AI-generated responses for hallucinations, incorrect financial advice, and compliance violations. Our survey found that 15-57% of teams report incorrect or incomplete AI agent responses as their top concern. In financial services, an AI agent providing incorrect fee information or failing to disclose terms creates the same regulatory exposure as a human agent error - but at higher volume and speed. Most QA tools in this comparison monitor human agents only. ##### QA-to-training integration Contact center QA tools that separate scoring from training create a compliance delay in fintech - the gap between identifying a regulatory skill gap and retraining the agent is where violations repeat. Our survey found that 53.5% of agents say the hardest part of ramping is applying training to real situations, and 82.5% feel prepared when starting but struggle in practice. Generic retraining doesn’t close that gap. Look for QA tools that connect findings to training automatically - ideally generating practice scenarios from real interaction patterns. ##### Security certifications and data handling When evaluating QA vendors, require documentation of current certifications - not planned certifications. SOC 2 Type 2 and ISO 27001 are the minimum thresholds for handling financial interaction data. PCI DSS compliance matters when the QA tool processes or stores payment-related conversation data. Data residency is an additional consideration for fintech teams operating across jurisdictions - some regulatory frameworks, including GDPR, require that personal data be stored and processed within specific geographic boundaries. A QA vendor storing interaction data in the wrong region creates a compliance risk of its own. #### The 10 best contact center QA software for fintech teams These 10 tools cover purpose-built QA platforms to QA modules within larger contact center suites and are evaluated on regulatory compliance capabilities, AI agent QA support, conversation coverage models, and QA-to-training integration. You can use this section to get a better feel for which QA software can help your fintech contact center maintain compliance, close skill gaps before violations repeat, and build a defensible audit position before a regulator asks for one. ##### 1. Solidroad (best for fintech teams needing 100% QA coverage with regulatory audit trails and integrated agent training) Solidroad is the only contact center QA platform in this comparison that treats QA as compliance infrastructure rather than a performance metric - scoring 100% of interactions, creating documented audit trails, and connecting QA findings directly to agent training, all built on 3 million scored conversations. Solidroad replaces manual QA sampling - which typically covers 1-5% of interactions - with automated QA scoring that evaluates every customer conversation across voice, chat, email, and video channels. For fintech contact centers handling regulated interactions, it’s the difference between having a defensible audit position and hoping regulators don’t ask about the 98% of conversations nobody reviewed. ###### Key differentiators - 100% automated QA coverage across every interaction: Solidroad is AI-native - built on AI from the ground up, not legacy QA with AI bolted on. It scores every conversation across voice, chat, email, and video automatically, creating a documented audit trail against compliance rubrics. “We sampled 2%” is not a defensible audit position. “We scored 100%” is. - Real-time compliance flagging: Solidroad flags potential UDAAP violations, churn signals, and brand risks during or immediately after each interaction - giving fintech teams the ability to act before violations accumulate into enforcement actions. - AI agent QA with hallucination detection: Most QA tools monitor human agents only. Solidroad monitors AI agent responses alongside human agents, detecting hallucinations and non-compliant responses before they compound. In financial services, every undetected AI hallucination is a regulatory liability. - QA-to-training integration: QA findings automatically trigger personalized training simulations - scored against company SOPs and regulatory requirements. The gap between “we found the violation” and “the agent can handle this correctly” shrinks from weeks to hours. ###### Key capabilities - Custom scorecards aligned to your workflows - Trend spotting and pattern detection across interactions - Multi-language support across training scenarios - Coaching workflows, including interaction tagging, notes, training assignments, and progress tracking - SOC 2 Type 2 and ISO 27001 certified - Integrations with Zendesk, Intercom, Gladly, Gorgias, Help Scout, and ServiceNow ###### Strengths Solidroad’s integrated QA and training platform closes the compliance delay that separate tools create - QA findings feed directly into personalized training simulations without a handoff to a separate L&D team or process. AI agent hallucination detection adds a layer of compliance monitoring that grows more critical as fintech teams deploy AI agents for customer-facing interactions - every AI response is monitored against the same compliance rubrics as human agents. ###### Limitations Custom pricing with no public tiers means teams need a demo call for details. One G2 reviewer noted that AI training simulations can respond before the agent finishes speaking - a timing issue in the simulation experience, not the QA scoring. Solidroad is purpose-built for QA and training. Teams that also need workforce management, speech analytics, or gamification will need additional tools alongside it. ###### What users say “What I like the most is how easy to use it is. The interface is simple but contains all I need to keep track of everything. It has been helpful in our hiring and training due to the features it has. And the best part is innovation. I’ve never seen something as cool as Solidroad before.” - G2 reviewer “The AI simulations are so closely related to actual human experience.” - G2 reviewer “Our relationship with Solidroad is at the forefront of our experiential learning initiative. The Solidroad team has been a pleasure to work with from the beginning.” - John, Brown & Brown, Product Hunt reviewer “This tool saves us so much when it comes to time and human resources, and delivers high-quality results.” - Product Hunt reviewer See how Solidroad works - book a demo to see how automated QA scoring creates regulatory-ready audit trails across your fintech contact center. ##### 2. Observe.AI (best for enterprise contact centers in regulated industries scaling QA coverage without expanding headcount) Observe.AI combines real-time speech analytics with automated QA scoring, emphasizing compliance monitoring and risk detection in voice interactions. It’s a strong option for enterprise contact centers in regulated industries that need to scale QA coverage without proportional headcount growth. - AI-powered transcription and scoring - Voice-first AI agents - Real-time agent guidance (AI Copilots) - Compliance and risk monitoring - 100% interaction coverage Observe.AI has built a broad product portfolio beyond speech analytics, with voice-first AI agents and real-time agent guidance. The platform targets enterprise contact centers in regulated industries, including financial services and healthcare, and its compliance monitoring features are designed for high-volume operations. “A wider product offering means that speech analytics is now a sliver of their entire product catalogue. They are constantly making advancements and bugs are rarely, if ever, an issue.” - G2 reviewer The biggest limitation flagged by real reviewers is concerns over accuracy issues. Whether it’s sentiment inaccuracy or mistakes with transcription , many users were not impressed by these issues. Observe.AI’s voice-first approach means QA capabilities center on speech analytics rather than the broader compliance monitoring architecture that fintech teams need across all interaction channels. ##### 3. MaestroQA (best for teams seeking structured evaluations and hands-on QA workflows with deep analytics) MaestroQA (recently rebranded to Rippit) offers structured QA evaluations with customizable scorecards, calibration tools, and deep analytics - a strong choice for teams that want granular control over their QA workflows, though its manual-first architecture contrasts with AI-native platforms that automate scoring at scale. - Customizable scorecards and calibration tools - QA-to-coaching workflows - Performance analytics and metrics tracking - Integration with Salesforce, Zendesk, Kustomer - Structured evaluation frameworks MaestroQA validated the enterprise QA market and proved that companies will fund dedicated QA tooling at enterprise scale. The platform offers deep analytics and structured evaluation workflows that give QA managers granular control over scoring consistency. Their 4.8 G2 rating reflects satisfaction among teams that want hands-on QA processes. The most common complaints from G2 reviewers center on usability and customization . The interface offers limited flexibility - no custom fonts, colors, or personal workflow tools like saved queues or notes. Reviewers also flag performance lags, a steep initial learning curve, and reporting dashboards that don’t bend easily to team-specific needs. ##### 4. NICE CXone (best for large enterprises needing 100% call recording with AI-powered QA at scale in regulated industries) NICE CXone is an enterprise-grade CX platform with built-in quality management, 100% interaction recording, and workforce management tools - suited for large financial services operations that need scale but may find the platform’s breadth comes at the cost of QA-specific depth. - 100% interaction recording - Quality management with AI evaluation - Interaction analytics - Workforce management - Automated notetaking NICE CXone is an established enterprise platform with 1,728 G2 reviews and a broad feature set covering QA, workforce management, and interaction analytics. For large financial services operations already using NICE products, the built-in quality management tools give teams regulatory-grade interaction recording at scale. NICE CXone is a broad platform where QA is one module among many. Fintech teams that need specialized compliance monitoring - UDAAP flagging, real-time risk detection, AI agent QA - may find the platform’s scope a trade-off against QA-specific depth. The pricing ($110-$249/agent/month) reflects the full platform, not just QA capabilities. ##### 5. CallMiner (best for large-scale operations with BI teams focused on compliance analytics and root cause analysis in regulated industries) CallMiner Eureka offers advanced conversation intelligence with AI and NLP-powered compliance analytics, root cause analysis, and keyword spotting - purpose-built for large contact centers with dedicated analyst teams in regulated industries, including financial services and banking. - Advanced conversation intelligence (AI/NLP) - Root cause and trend analysis - Keyword and phrase spotting for compliance - Call monitoring and recording - Compliance program building CallMiner has deep expertise in compliance analytics for regulated industries, with features specifically designed for building compliance programs from conversation data. Their 223 G2 reviews and focus on root cause analysis make them a strong option for large financial services contact centers with dedicated analyst teams. CallMiner requires dedicated BI and analyst teams to operate effectively. The platform’s depth comes with a steep learning curve, and the implementation overhead is heavier than purpose-built QA platforms. Fintech teams without dedicated analytics staff may find the time-to-value longer than more automated alternatives. ##### 6. Scorebuddy (best for teams wanting flexible scorecards and AI-powered QA with strong calibration features) Scorebuddy offers customizable scorecards, GenAI Auto Scoring for 100% coverage, and integrated calibration tools - including a financial services compliance module that makes it a relevant option for fintech contact centers that want structured QA with AI automation. - Customizable scorecards - GenAI Auto Scoring (100% coverage) - Calibration tools - Integrated coaching - Financial services compliance module - Reporting dashboards Scorebuddy’s financial services compliance module is one of the few industry-specific QA features in this comparison. Their calibration tools and customizable scorecards give QA managers control over scoring consistency, and the GenAI Auto Scoring feature extends coverage beyond manual sampling. Scorebuddy’s financial services compliance module adds industry-specific value, but the platform remains a QA-only tool. QA findings feed into coaching workflows but don’t automatically generate training simulations. For fintech teams, this leaves a compliance remediation gap: The time between identifying a regulatory violation pattern and retraining agents falls to separate L&D processes. ##### 7. Level AI (best for mid-sized contact centers focused on compliance monitoring and real-time analytics) Level AI provides AI-powered auto-scoring, compliance detection, and conversation intelligence dashboards that are ideal for mid-sized contact centers. - Auto-scoring and compliance detection - Advanced topic and sentiment analysis - Conversation intelligence dashboards - Real-time QA - Agent coaching integration Level AI’s customizable dashboard features give QA managers visibility into conversation data across multiple dimensions. The platform’s compliance detection capabilities and real-time analytics are designed for teams that need ongoing monitoring rather than periodic QA reviews. For fintech teams, Level AI’s reported 24-hour call ingestion delay limits real-time compliance monitoring. In financial services, where regulatory violations need to be flagged during or immediately after an interaction, delayed ingestion creates a window where compliance issues go undetected. ##### 8. Talkdesk (best for regulated industries wanting pre-built compliance workflows) Talkdesk is a full contact center platform where QA is one component of a broader operational suite - offering Industry Experience Clouds pre-built for banking, insurance, and healthcare with compliance workflows, AI-powered engagement, and 70+ integrations. - Industry Experience Clouds (banking, insurance) - AI for omnichannel engagement - AI for customer experience analytics - Pre-built compliance workflows - 70+ integrations and 100+ marketplace apps Talkdesk’s Industry Experience Clouds for banking and insurance remove the need to build compliance workflows from scratch - regulatory requirements are packaged into the platform out of the box. For large financial institutions with complex procurement processes, the 70+ native integrations and transparent pricing ($85-$225/user/month) reduce implementation friction and give compliance teams clear criteria to evaluate against. Talkdesk is a full contact center platform where you’re unable to purchase their QA module as a standalone. This means that fintech teams with existing telephony infrastructure may find the platform migration overhead disproportionate to the QA benefit. ##### 9. EvaluAgent (best for teams wanting full-suite QA with evaluation workflows, coaching, and agent engagement) EvaluAgent provides evaluation workflows, AI auto evaluation, coaching tools, and agent engagement features with strong Freshdesk integration - suited for contact centers that want structured QA processes with insight generation. - Evaluation workflows - Coaching tools and agent engagement - AI auto evaluation - Insight generation - Freshdesk integration EvaluAgent’s AI auto evaluation reduces the need for frequent QA analyst intervention, and the platform’s insight generation provides actionable summaries from evaluation data. Integration with Freshdesk makes it a natural fit for support teams already using the platform. EvaluAgent covers evaluation and coaching, but doesn’t integrate training simulations with QA findings. For fintech teams needing fast compliance remediation, the gap between identifying a violation pattern and rebuilding agent capability remains a manual process. ##### 10. Playvox (best for teams using Salesforce or Zendesk wanting AI forecasting, QA automation, and workforce management) Playvox combines AI-powered workforce forecasting with quality management and performance tracking, deeply integrated with Salesforce and Zendesk - an option for teams that want QA automation alongside workforce planning. - Workforce management with AI forecasting - Quality management with AI scoring - Performance tracking - Deep Salesforce/Zendesk integration - Capacity planner Playvox’s deep integration with Salesforce and Zendesk makes it a natural extension for teams already invested in those ecosystems. The combined workforce management and QA platform reduces tool fragmentation, and AI-powered forecasting adds operational planning alongside quality scoring. Playvox combines workforce management (WFM) and QA, but doesn’t offer AI agent hallucination detection or training simulations. For fintech teams that need compliance-focused QA with agent upskilling, the platform’s WFM focus adds operational planning without addressing the regulatory monitoring and audit trail requirements that fintech compliance teams prioritize. #### How to choose the right contact center QA software for fintech Fintech teams choosing contact center QA software face a market where most vendors position their tools as compliance-ready without the architecture to back it up. General-purpose QA platforms can monitor interactions and score agents - but fintech compliance requires more than performance monitoring. The four questions below are designed to separate tools built for regulatory audit readiness from those that treat compliance as a feature add-on. Use the answers as a filter, not a scorecard. A vendor that can’t demonstrate a documented audit trail, explain how they monitor AI agent responses, or show current security certifications hasn’t met the baseline for fintech compliance infrastructure - regardless of how strong their other features are. The goal isn’t to find the tool with the best answers across all four. It’s to eliminate vendors that fail on the criteria that matter most for your regulatory environment before you get deep into procurement. Does it create a documented audit trail that survives regulatory inquiry? Recording conversations isn’t the same as scoring them against compliance rubrics. Ask vendors to demonstrate how their platform documents QA findings in a format a compliance officer can present to regulators. Does it monitor AI agent responses alongside human agents? Most QA tools in this comparison monitor human agents only. If your team deploys AI agents for customer interactions - or plans to - QA coverage gaps create regulatory exposure that grows with AI adoption. Does it connect QA findings to agent training automatically? The time between identifying a compliance gap and retraining the agent is where violations repeat. Ask how QA findings translate to training - and whether that process requires a separate L&D team and timeline. Does it hold SOC 2 Type 2 or ISO 27001 certification for handling financial interaction data? Planned certifications aren’t current certifications. Require documentation of active compliance before sharing customer interaction data with any QA vendor. #### Frequently asked questions ##### What is contact center quality assurance software? Contact center quality assurance software evaluates customer interactions against predefined standards - scoring agent performance on accuracy, compliance, and customer outcomes across voice, chat, email, and video channels. In fintech, QA software serves a dual purpose: improving agent performance and creating documented evidence of regulatory compliance. The tools range from manual scorecard platforms to AI-powered systems that automatically score 100% of interactions against compliance rubrics. ##### What features should I look for in contact center QA software for financial services? Fintech teams should prioritize five features beyond standard QA capabilities: automated 100% conversation scoring (not sampling), real-time compliance flagging for UDAAP and FCA violations, documented audit trails searchable by interaction type and date range, AI agent monitoring alongside human agent QA, and SOC 2 Type 2 or ISO 27001 certification. Secondary priorities include customizable compliance scorecards, integration with your existing support platform (Zendesk, Intercom, Gladly), and QA-to-training automation that closes skill gaps without separate L&D processes. ##### How do regulated industries like fintech choose QA software differently? Regulated industries evaluate QA software as compliance infrastructure, not performance tooling. Three factors differentiate fintech-ready QA: 100% conversation coverage that creates searchable audit trails, real-time compliance flagging for industry-specific regulations (UDAAP, FCA Consumer Duty, PCI DSS), and documented QA processes a compliance officer can present to regulators. Standard features like scorecard customization and coaching are table stakes - the compliance infrastructure layer separates fintech-ready QA from general-purpose tools. ##### Can QA software monitor AI chatbot responses as well as human agents? It depends. Most contact center QA tools in this comparison monitor human agent interactions only. Only Solidroad offers dedicated AI agent QA with real-time hallucination detection - monitoring AI-generated responses for incorrect information, compliance violations, and fabricated details before they reach customers. As fintech companies deploy AI agents, the gap between human agent QA and AI agent QA becomes a compliance risk: an AI agent providing incorrect fee information creates the same regulatory liability as a human agent error, but at higher volume. ##### What compliance certifications should contact center QA software have for financial services? Fintech teams should require three certifications as minimum thresholds: SOC 2 Type 2 (demonstrates ongoing security controls, not a point-in-time snapshot), ISO 27001 (systematic information security management), and PCI DSS compliance when the QA tool processes payment-related interaction data. Solidroad holds SOC 2 Type 2 and ISO 27001 certifications. Verify data residency policies if your operations span jurisdictions - some regulatory frameworks require that interaction data remain within specific geographic boundaries. #### Regulatory risk is the QA problem fintech can't afford to ignore The cost of consumer-facing compliance failures in fintech compounds with scale. Every unreviewed conversation is a regulatory liability that grows until a regulator surfaces it. HSBC's £185 million redress resulted from insufficient call quality assurance that had accumulated undetected. Apple and Goldman's $89 million penalty stemmed from back-end dispute processing failures that went unresolved. Both cases demonstrate what happens when compliance monitoring gaps persist - violations compound until regulators surface them. General-purpose QA tools are built to improve agent performance scores. Compliance-ready infrastructure is built to answer a different question: If a regulator asked for documentation of every fee-related conversation from last quarter, could you produce it? The difference matters when a regulator asks for documentation of every fee-related conversation from last quarter. The question for fintech contact center leaders isn't whether to invest in QA software - it's whether their tool treats QA as a performance metric or as regulatory infrastructure. The 81% of conversations that go unreviewed are where the risk concentrates. #### See how Solidroad protects fintech teams Solidroad scores 100% of customer interactions, flags compliance violations in real time, and automatically trains agents on regulatory scenarios. See how Solidroad works - book a demo to see audit trail documentation, hallucination detection, and QA-to-training integration for your fintech contact center. #### More articles Announcements Sep 14, 2026 ###### Solidroad and Customer Intelligence Platform Unwrap Partner to Turn Insight Into Action Jul 29, 2026 ###### Solidroad Named OpenAI Select Partner Quality Assurance Jul 10, 2026 ###### How to QA AI Customer Service Agents