How do we test AI agents` customer interactions?

We are your hidden customers

The problem isn’t your agent`s productivity. It’s that no one knows how they behave with your customers in real life. We do. Instead of dealing with unpredictable outputs and negative reviews, we know how to make things predictable and better.

Evaluation map and unique metrics you can only get through our tests

Without an independent check, all standards tend to drift. We act as a reality check between what you think is happening and what customers actually experience, including instruction adherence under ambiguity, hallucination rate, recovery and self-correction ability, consistency across identical scenarios etc.

Dual-layer evaluation

Within each scenario, we create structured variations: user persona, intent, task success, accuracy, clarity, tone, effort, trust, policy compliance, escalation correctness etc. This let us know why your AI agents fail.

Failure taxonomy checks

Instead of just scores and cases, we classify how and why the agent fails: misunderstood intent, correct answer/poor explanation, hallucinated information, refused when it shouldn’t, didn’t escalate when needed etc. This is what actually drives customer interaction improvement.

Red team & Gray hat stress-testing

We perform critical test before making your AI agents public, like attempts to extract sensitive data, requests for disallowed actions, manipulative or adversarial phrasing, policy violation rate under adversarial prompts etc.

RLHF vulnerabilities check

Because RLHF (Reinforcement Learning on Human Feedback) relies on thousands of human preference rankings, compromised datasets introduce severe security risks: rank-poison, jailbreaks, adversarial prompts, sycophancy & deceptive alignments.

Process-based supervision

We use Step-level Feedback: the model receives rewards or corrections for every individual line of code, math equation, or logical deduction, Process Reward Models (PRMs): specialized models built to judge whether an individual step in a chain-of-thought is correct and Credit Assignment: by grading the steps, the agent learns exactly where an error occurs.

Adversarial annotators & testers

We don’t just ask annotators and testers to rate your agents, we ask them to try to break the agent, trick it into mistakes and push it into edge cases. This dramatically improves robustness

Information density and clarity

Most AI agents fail because they optimize for: volume of data, instead of information density and clarity. We tune them up to provide better user experience - users prefer models that deliver the highest signal-to-noise ratio, fewer filler words, no repetition, and a faster path to the answer.

Stress-test AI agents before they become support tickets

Hope isn't testing. Don't let customers test your AI.

Join the waitlist to test your AI agents before your customers do.

How-it-works