We test AI the way real customers behave.
Trained human testers call your voice agents the way real customers do — impatient, skeptical, off-script — then hand engineering a scored failure map and the fixes to ship first. The same rigor applies to your AI SDRs, chatbots and support AI.
↑ Illustrative voice test — not a client report
We evaluate every AI your customers talk to.
Voice is the wedge — the hardest surface to get right. But the operation runs deeper. Each line is scored on its own rubric, validated through the same three-layer QA system, and delivered as a report your engineers can act on.
Voice Agent Testing
Real humans on live calls, finding what only surfaces under interruption, latency, accents, and topic shifts — the failures a text transcript never shows. The fastest, sharpest read on whether your agent holds up with a real caller on the line.
Explore voice testingScored on the Voice rubric: intent recognition, accent handling, response accuracy, context retention, conversation quality.
AI Sales / SDR Testing
Where qualified conversations leak — objection handling, discovery, positioning, and the booked meeting.
Scored on the Sales rubric: qualification, discovery, positioning, objections, booking.
View serviceCustomer Support AI Evaluation
Whether the agent actually resolves — accuracy, resolution, and the handoff when it cannot.
Scored on the Support rubric: accuracy, resolution, escalation, experience, consistency.
View serviceContact Center AI QA Program
Ongoing QA across high volume — accuracy, adherence to your own scripts, and resolution at scale.
Scored on the Contact Center rubric: accuracy, experience, policy & script adherence, resolution, escalation.
View serviceEnterprise AI Performance Assessment
A broad, cross-functional read on a production system — reliability, accuracy, and operational risk.
Scored on the Enterprise rubric: accuracy, experience, reliability, escalation, adherence, effectiveness.
View serviceYour AI passed internal tests. Then it met a human.
Internal evals run clean scripts. Real users don't. These are the behaviors that break agents in the wild — and exactly what our testers reproduce on purpose.
Four moves from your AI to a failure map.
Human testing
Trained testers run designed scenarios and adversarial conversations against your live agent.
Evaluation & scoring
Every interaction is scored on a structured rubric by a separate QA review layer.
Failure mapping
We categorize each break — what triggered it, where, and how often it recurs.
Recommendations
You get a prioritized, engineer-ready report of what to fix first and why.
What a pilot puts in your hands.
A failure map and a scorecard you can hand straight to engineering — an excerpt of the Conversation Intelligence Report. Below is an illustrative example built to show the format, not a real client result.
Outputs from every pilot.
Every finding is ranked by customer impact, how often it recurs, and how cleanly the agent recovers.
Annotated transcripts
Timestamped interactions with reviewer notes and failure markers.
Failure map
Breaks grouped by failure type, frequency, and impact.
Severity-ranked findings
Issues prioritized by business risk and customer impact.
Rubric scores
Consistent scoring across handling, accuracy, flow, and recovery.
Reproduction steps
The exact prompts and conditions needed to recreate each failure.
Prioritized recommendations
Engineer-ready fixes ordered by expected impact.
Supporting evidence — full call recordings and transcripts from every evaluated interaction — is included with each pilot.
Internal testing is necessary. Independent testing finds different failures.
Your team knows how the system is supposed to work, so they test the paths it was built for. Our testers don't. They arrive with incomplete information, the wrong assumptions, and the impatience of a real customer — and that distance is exactly what surfaces the failures your own evals never trigger.
- An external layer, separate from the team that built the agent
- Adversarial behavior and ambiguity no internal script anticipates
- Judgment on tone and trust, not just whether the answer was correct
- Run by an ISO-certified operation with U.S. call-center experience
Not a panel of freelancers. A certified evaluation operation.
The evaluation is delivered by KNK Global — an ISO 27001 and ISO 9001-certified, Pakistan-based operation with years of U.S.-facing contact-center experience. English-fluent, trained for conversational-AI evaluation, and structured so scoring is consistent, auditable, and reproducible — not one tester's opinion.
Execution
Trained evaluators run the conversations, label every output, tag failure types, and attach a confidence score.
Validation
A separate QA layer reviews the work, corrects labeling errors, and checks consistency across evaluators.
Authority
A final evaluation authority resolves disputes, locks the scoring rules, and signs off the report before delivery.
Test once, every release, or continuously.
Pilot
- › One voice agent, full scenario sweep
- › Failure map + scorecard
- › Prioritized fixes + retest plan
Release-Cycle Testing
- › Regression suite before each release
- › Catch regressions before users do
- › Trend reports across versions
Monthly QA Retainer
- › Dedicated testing capacity
- › Ongoing monitoring & scoring
- › Standing engineering reports
Find out exactly where your AI breaks.
We run a pilot validation cycle within days and hand you a failure map, a conversation breakdown and a conversion-leakage analysis.