Every interaction scored. At the accuracy a supervisor would sign.
Full coverage has been table stakes since 2024. The question is whether the score is right. QEval® grades every conversation at a 94%+ contractual accuracy SLA, while the industry averages 65 to 70%. Paste a real transcript below and watch it score, no login.
Your AI vendor grades its own homework.
You run an independent QA team for your human agents. For your AI agents, the company that built them also grades them, and you take that number on faith. Two things make that untenable at scale.
The vendor scores its own bot.
The company that built your AI agent reports whether it is working, often using its own model to grade it. When the bot states a policy that does not exist, the regulator and the customer come to you, not the vendor. QEval® is the independent layer. It did not build the agent, so it has no number to protect.
It is wrong the same way on every call.
Human-era QA samples 2 to 5 percent of interactions because human error is random. AI error is not. A model that mishandles a disclosure or invents a policy does it on every matching call. You can read 5 percent and miss the pattern running on 100 percent of them.
Hallucination
The agent states a refund window, a policy, or an eligibility rule that does not exist, with full confidence, on every matching call.
Compliance miss
A required disclosure, a recording notice, or an identity check is skipped or reworded below the regulated standard.
Brand-voice drift
The agent slips out of your voice, over-promises, or adopts a tone the brand would never approve, gradually and at scale.
Off-script behavior
The agent loops, repeats near-identical phrases, refers to itself as an AI, or makes a commitment your policy never approved.
ASR errors
Speech-to-text misfires on accents, crosstalk, phone-line noise, names, and spoken numbers. Word error rates climb as the audio degrades, and one wrong word becomes the wrong intent and the wrong action.
Voice hallucination
The speech layer fabricates words that were never spoken, surfacing a name, a number, or a policy the caller never said, then acting on it as if it were real.
Latency and dead air
The agent lags mid-conversation or talks over the caller. Past roughly 700 milliseconds the pause reads as dead air, and the customer repeats, interrupts, or abandons.
Escalation and handoff failure
The agent does not recognize when it is stuck, so the caller loops, and when a handoff finally happens, the context is lost and the customer starts over with a human.
Pick a sample. See what your dashboard missed.
Every competitor page in this category shows you a static dashboard. This one runs. Pick a sample or paste any conversation. QEval® detects the speakers, scores every turn, and surfaces compliance, empathy, resolution, and brand voice in real time.
Tests whether QEval® rewards a Sierra AI agent that acknowledges repeat contact, commits a real refund, and opens a supervisor pathway, while still watching disclosure and churn risk.
Works with the AI agents you already bought.
QEval® scores these AI agent platforms as data sources, not competitors. Your AI vendor's own dashboard reports whether the bot is busy. QEval® reports whether it is good, on the rubric you already trust.
Your humans and your AI agents, graded on the same rubric.
Most teams run AI agents in one tool and human QA in another, with no shared standard between them. QEval® puts both workforces on a single scorecard, so a quality leader can compare them line for line and govern them as one operation.
We do not suggest a prompt tweak. We deliver a fix package.
A recommendation is a sticky note for an engineer. When QEval® finds a deflection failure, it emits a machine-readable fix package, JSON or XML, that your AI agent's own stack, knowledge base, or ticket queue can consume. It carries the deflection failure reason, the failure time, the failure intent, and a KB recommendation, plus the evidence and a regression test. A human approves it, then it is applied. This is a demonstration of capability, not a customer result.
The deflection failure fires on the exact turn
QEval® catches the moment the AI agent fails to contain the conversation: a looped policy, a missed disclosure, an invented rule. The signal logs with the precise transcript span.
Classify the failure, intent, and recurrence
The finding is scored on the same scorecard a human would be held to, classified by failure category and intent, then matched against every other conversation carrying the same pattern.
Write the fix package and route it
QEval® emits the JSON or XML package and delivers it by webhook or API into the agent stack, the knowledge base, or a ticket. No engineer reads a dashboard and retypes the fix.
A regression test ships with the fix
Each package carries a regression test, so the next batch of conversations is re-scored to confirm the same failure cannot quietly return.
{
"fix_package_id": "fpkg_2026-05-29_8f3a21c7",
"schema_version": "1.0",
"generated_at": "2026-05-29T14:32:07Z",
"target_agent": { "vendor": "sierra", "channel": "voice" },
"failure": {
"deflection_failure_reason": "kb_article_missing",
"failure_time": "2026-05-29T14:18:53Z",
"failure_intent": "billing_dispute.duplicate_charge",
"failure_category": "knowledge_gap",
"severity": "high",
"outcome": "escalation_to_human"
},
"diagnosis": {
"root_cause": "missing_kb_article",
"confidence": 0.93,
"recurrence": { "count": 23, "window_days": 7, "trend": "rising" }
},
"kb_recommendation": {
"action": "create_article",
"proposed_title": "How to dispute and reverse a duplicate charge",
"target_kb_system": "zendesk_guide",
"target_section": "Billing / Disputes"
},
"regression_test": {
"test_id": "rt_duplicate_charge_001",
"pass_criteria": "resolution_score >= 80 AND no policy_loop"
},
"lifecycle": { "status": "open", "priority": "P1" }
}
<?xml version="1.0" encoding="UTF-8"?> <fix_package id="fpkg_2026-05-29_8f3a21c7" schema_version="1.0"> <generated_at>2026-05-29T14:32:07Z</generated_at> <target_agent vendor="sierra" channel="voice"/> <failure> <deflection_failure_reason>kb_article_missing</deflection_failure_reason> <failure_time>2026-05-29T14:18:53Z</failure_time> <failure_intent>billing_dispute.duplicate_charge</failure_intent> <failure_category>knowledge_gap</failure_category> <severity>high</severity> </failure> <diagnosis root_cause="missing_kb_article" confidence="0.93"> <recurrence count="23" window_days="7" trend="rising"/> </diagnosis> <kb_recommendation action="create_article" target_kb_system="zendesk_guide"> <proposed_title>How to dispute and reverse a duplicate charge</proposed_title> </kb_recommendation> <lifecycle status="open" priority="P1"/> </fix_package>
One failure, every conversation it touches.
An AI agent does not fail once. It fails the same way across every matching call. QEval® clusters identical failures so you see the true exposure and emit one fix package for the root cause, not forty for the symptoms. A high containment rate hides these. The repeat-contact rate does not.
We score Voice AI at production scale, not in a lab.
QEval® processes more than 1 million Voice AI calls a day. Every call is scored against the same scorecards, disclosures, escalation rules, and brand-voice standards a real operation enforces, human agent or AI agent, on one standard. Scoring Voice AI at this volume is how QEval® catches the systematic failure a 2% sample never sees: the intent that always breaks, the disclosure that is always missed, the answer that is wrong the same way on every call.
A proprietary Mixture-of-Experts model, not a wrapper.
QEval® runs on a closed-source Mixture-of-Experts architecture that ETS Labs owns, trains, and operates. Each scoring dimension routes to a purpose-trained expert sub-model rather than a single generalist. For an AI-agent program, the architecture matters most for data sovereignty.
Your data never trains a third party
Customer interaction data never enters a third-party foundation model's training loop. QEval® is not a fine-tuned layer over OpenAI, Anthropic, or AWS Bedrock.
PII redacted before any model sees it
PII and PHI are redacted at ingest via Named Entity Recognition, before any model processing. No unredacted data reaches the experts.
Every decision is auditable
Each classification traces to the specific expert sub-model and the transcript span that triggered it, which is what makes an AI-governance program defensible.
AI Agent QA is Layer 1 applied to a new agent type.
Grading an AI agent for quality and compliance is Layer 1 work. The value compounds when those scores feed the layers above: how the AI workforce affects operations, and how a leader governs a blended human-and-AI workforce from one strategic view.
A QA tool stops at Layer 1. QEval® treats the AI agent as one more workforce to govern across all six layers, which is what turns scoring into strategic oversight of a human-and-AI operation, not just a pass-fail mark on a bot.
Straight answers about grading AI.
We just bought an AI agent. Why a separate tool to grade it?
Because the vendor that built the agent is also the party graded by its own dashboard. QEval® is vendor-neutral: an independent quality layer that scores the AI agent against your scorecard, your brand voice, and your compliance rules, not the vendor's definition of success. The same separation of duties you would expect for human QA applies to AI.
Doesn't the AI vendor's own dashboard tell me if it's working?
It tells you the agent is busy: deflection rate, containment, volume. It does not tell you whether the agent stayed compliant, stayed on brand, or hallucinated a policy on a recurring intent. QEval® grades the conversation quality the vendor dashboard does not measure, and it does so on the same rubric as your humans so the two are comparable.
How do I catch hallucination or drift before customers do?
Score every conversation, not a sample. Because AI failures are systematic, they show up across many interactions at once QEval® scores 100 percent of conversations and flags repeated phrasing across AI turns, missed disclosures, and off-brand language as patterns, not one-off anomalies. The drift example in the live scorer above shows the detection firing on a looping AI agent.
Can one scorecard cover both my humans and my AI?
Yes, and that is the point. QEval® applies a single scorecard, with the same 94%+ classification-accuracy SLA, whether the agent is human or AI. A quality leader can compare both workforces line for line and govern them as one operation rather than maintaining two disconnected quality programs.
Human, AI, or anything in between.
Bring your scorecards and the AI agents you already run. We will score a real AI conversation in 30 minutes and show you the systematic pattern your current program missed last week.