Certify your AI agent before your regulator asks about it.
Test Agent Lab runs thousands of simulated customer conversations against your voice, chat, or web agent and scores every interaction against the standards that apply to your institution — model risk governance (SR 11-7), consumer disclosure (CFPB), and sector-specific compliance requirements — so your risk and compliance teams have documented proof before every release, not after a complaint or exam.
- 3,000+
- Simulated interactions per release — audit-ready evidence, not a sample
- Model risk governance aligned
- Scoring mapped to the standards your risk committee already reports against
- 100%
- Conversations documented — every score traceable to a transcript
Built for regulated financial services firms — not generic call centers.
Banks and credit unions ($1B–$10B in assets), insurers, and investment management firms where the digital or IT team owns configuration of the AI agent layer — even if live overflow is handled by a BPO.
Validate every AI Agent release with confidence.
Before rolling out a new version of your AI phone agent, run a comprehensive test campaign that measures customer experience, accuracy, compliance, latency, and more.
Our AI Agents call your IVR
Our AI voice agents run real customer scenarios — claims, billing, appointments — and call your IVR like real customers.
Every conversation is evaluated
Every call is recorded, transcribed, and scored turn by turn against the expected outcome, compliance rules, and conversation quality.
You get a release certification report
A single go/no-go verdict backed by a compliance-graded score, critical-fail findings by regulation, and per-persona breakdown — the same document your risk committee would ask to see.
Five evaluation categories. One composite score. Zero blind spots.
Every simulated call is graded against a regulation-aware rubric. Category scores roll up into a composite you can trend release over release — and any critical compliance or safety failure is surfaced separately, never averaged away.
Task resolution & efficiency
Did the agent actually solve the customer's problem? First-contact resolution, escalation appropriateness, time and turns to resolution.
Accuracy & reliability
Intent recognition, factual accuracy, hallucination rate, policy and procedure adherence, and consistency across rephrased questions.
Conversational quality
Tone and empathy, brand voice consistency, response relevance, and how natural the agent sounds turn by turn.
Compliance & regulatory risk
Required disclosures, identity verification adherence, prohibited-content avoidance, and complete audit trails for model-risk documentation.
Safety & failure handling
Graceful degradation, vulnerable-customer detection, error recovery, and appropriate refusal of out-of-scope requests.
Generic LLM evaluators catch obvious failures. They miss the subtle ones — a disclosure buried mid-sentence, a suitability answer that oversteps policy, a tone mismatch with a distressed caller. Test Agent Lab's Judge Agent is tuned to your institution's specific regulatory profile, not a generic checklist.
Objective measurements are tracked separately so latency spikes and tail behavior stay visible instead of getting diluted inside a score.
Listen to an agent test an IVR in real time.
One scenario, one call, scored end to end. This is the raw output you'd see in your weekly report.
Sample player — production reports include the full MP3, downloadable transcript, and per-turn scoring.
What you get after each test:
A full audit-ready report — composite score, critical-fail findings, per-persona breakdown, transcripts, and prioritized remediations.
IVR Voice Banking Assistant — Quality & Compliance Assessment
| Persona | N | Composite | Critical fails | Status |
|---|---|---|---|---|
| Routine Balance / Account Inquiry | 850 | 93 | — | Pass |
| Payments & Transfers | 620 | 90 | 2 | Pass |
| Card Dispute / Chargeback | 410 | 82 | 8 | Watch |
| Loan Application Inquiry | 340 | 84 | 4 | Watch |
| Angry / Escalated Customer | 280 | 78 | 8 | Watch |
| Confused / Elderly Caller | 240 | 74 | 10 | Remediate |
| Non-Native Speaker | 110 | 81 | 1 | Watch |
| Fraud Report / Distress | 150 | 68 | 12 | Remediate |
- HIGHClose transfer-and-fraud-intake disclosure gaps in the Fraud/Distress flow.
- HIGHEnforce second-factor identity verification before any balance disclosure.
- HIGHRe-ground APR, fee, and dispute-timeline responses against live policy tables.
- MEDAdd distress + hardship co-occurrence as a vulnerability-detection trigger.
- MEDOptimize real-time lookup path to reduce P90 latency and turn count.
Two founders, one obsession: making phone systems behave.
Nima Bahrehdar is a product and AI strategy leader with over 20 years of experience building enterprise software and leading digital transformation initiatives across financial services, technology, and consulting. Throughout his career, he has helped organizations design, launch, and scale AI- and data-driven products at companies including Amazon, McKinsey, Fidelity Investments, and Citizens Bank. His work has focused on turning emerging technologies into practical solutions that improve customer experience, operational efficiency, and business outcomes.
As the founder of Test Agent Lab, Nima is focused on one of the biggest challenges in enterprise AI: ensuring AI agents are reliable before they reach customers. Test Agent Lab helps organizations automatically test conversational AI at scale using AI-powered customer simulators, objective evaluation frameworks, and detailed performance analytics.
Nima holds an MBA from Boston University and an AI & Machine Learning executive certificate from the Massachusetts Institute of Technology. He is passionate about building products that solve real customer problems and believes the future of AI will belong to organizations that treat quality, testing, and governance as core product capabilities—not afterthoughts.
Andre Wehe is a software engineer, entrepreneur, and systems architect with more than 20 years of experience building large-scale distributed systems, SaaS platforms, and intelligent software products. Throughout his career, he has held technical leadership roles at Amazon Robotics and Evolv Technology, and as the technical co-founder of nDash, where he architected and built the company's cloud platform from the ground up.
Under Andre's technical leadership, nDash grew into a content marketing platform trusted by thousands of organizations, including Oracle, Visa, Kohler, Bitdefender, Zeta, and Epsilon. He has also led the development of mission-critical cloud services supporting Amazon's global fleet of more than one million autonomous mobile robots, as well as AI-driven enterprise products deployed in high-traffic environments.
As Technical Co-Founder of Test Agent Lab, Andre is building the next generation of quality assurance for conversational AI. He believes AI agents should be tested with the same rigor as traditional software, enabling enterprises to deploy voice and chat experiences with confidence through automated testing, intelligent evaluation, and continuous quality monitoring.
Andre holds a Ph.D. in Computer Engineering and Computer Science from Iowa State University and a German Diplom-Ingenieur (Dipl.-Ing.) in Electrical and Computer Engineering from the University of Applied Sciences Bielefeld, Germany.
Bob Levy is Founder & CEO of Immersion Analytics, where he pioneers patented visualization and rendering technology that makes complex data and AI models understandable and commercially useful. He brings over 25 years of product and engineering leadership from IBM (Rational Software, WebSphere), MathWorks, and Harte-Hanks Trillium Software, and serves on the board of OSINT security platform LifeRaft. He also advises nDash and Product Growth Leaders, helping teams apply AI without losing sight of the human side of product discovery.
As an advisor to Test Agent Lab, Bob brings deep, hands-on expertise in AI/ML systems, data visualization, and rigorous software testing — including his own patented work on test modularization and automation. He's fluent in the inner workings of large language models and neural networks, and has shared that expertise at MIT Technology Review's EmTech, MIT Media Lab's AR in Action, and O'Reilly Strata Data Conference. Bob holds a BSc in Computer Science and an MBA, both from Dalhousie University.
See your compliance score before your next exam does.
Request a scoped pilot on one flow. You'll get a full release certification report — critical-fail findings, disclosure compliance, transcript evidence — before you commit to anything.