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.
Watch it score a call.
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. It has scored 3,127,000,000 conversations so far this year.
Tests whether QEval rewards a fast repeat-contact acknowledgement, refund resolution, and supervisor pathway while still watching disclosure and churn risk.
This is a scoped, in-browser version. The production engine runs the same four expert pathways across 35+ languages and six channels at 94%+ accuracy.
One scorecard. The right expert on every line.
A general-purpose model scores compliance, empathy, resolution, and brand voice with the same weights, and the accuracy shows it. QEval® routes each item on your scorecard to a purpose-trained expert. That is how the accuracy holds across every dimension at once, not just one benchmark task.
Build the scorecard
Bring the scorecard your QA team already uses. Weighted items, compliance gates, empathy indicators, resolution markers, brand voice rules. Customizable down to the line item, no rebuild required.
The Classification engine routes it
Each scorecard item is mapped to the right pathway. Compliance language goes to a compliance expert. Empathy goes to an empathy expert. Brand voice goes to a brand voice expert. A 47-item scorecard becomes 47 routed classifications.
The Vocabulary library grounds it
Contact center language is not general English. Hold procedures, transfer protocols, disclosure requirements, de-escalation patterns. A proprietary lexicon tuned across 35+ languages gives the experts the domain fluency a generic model lacks.
Score and generate in one pass
The same evaluation produces the composite score, the four category breakdowns, the AI call summary, and the case notes. The agent does not write the wrap-up. That is up to 60 seconds saved per interaction, measured across enterprise deployments.
Monitor compliance as it happens
Recording disclosure, mini-Miranda, identity verification, cease-contact requests. Compliance monitoring runs continuously, not on a 2% sample, so a systematic miss is caught the first week, not the next audit.
Calibrate against your humans
Objective, repeatable scoring with calibration variance under 2%. Two evaluators no longer score the same call ten points apart. The number is defensible in a dispute and stable across the program.
"Our CCaaS already does QA."
It is the most common thing a buyer says, and it is half true. The CCaaS QA module samples interactions and runs post-call analytics. That is not the same as scoring every conversation at a contractual accuracy. The difference is what gets through.
Sampling-based post-call analytics
- Scores a sample of interactions, then extrapolates. Systematic errors in the unscored 95%+ stay invisible.
- Accuracy is unstated. There is no SLA, no recall figure, no calibration commitment to hold it to.
- One generalist model grades every dimension, so nuance like empathy and brand voice is the weakest part.
- Tied to the conversations inside that one platform. The average contact center runs 3.9 platforms.
- QA is a reporting feature bolted onto a routing product. It was never the core.
Full-coverage scoring at a contractual accuracy
- Scores every conversation, not a sample. A systematic error is caught the first week it appears.
- 94%+ accuracy SLA, 98%+ compliance accuracy, 95%+ violation recall, all written into the agreement.
- A dedicated expert on every dimension, so empathy, resolution, and brand voice hold the same accuracy as compliance.
- Vendor-neutral across 80+ integrations. One scorecard spans every platform you run, human and AI.
- Scoring is the entire product, built by an operator who runs the floor it grades.
Stop running QA work one task at a time. Dispatch it.
The score is the start of the work, not the end. The weekly report, the ad-hoc analysis, the re-audit against a new policy, the coaching write-ups: a QA manager used to do these by hand, in sequence. QEval® gives you operators you assign the work to and run in parallel. Each one is bounded to your scorecard and cites the conversations behind every answer. Press dispatch.
Other vendors ship one or two task bots on a fixed quality template. Generic analytics tools run agents in parallel but have no idea what your scorecard says or which compliance rule applies. QEval® operators sit on top of the scored interactions themselves. Every output is bounded to your scorecard, traces to the conversations behind it, and runs alongside the others, so the work that used to fill an analyst's week gets returned while you are still in the meeting.
Capacity, not a headcount cut.
Automating the scoring does not mean fewer QA people. It means your QA people stop spending their week listening to a 2% sample and start doing the work that only a human can: calibration, coaching, and the hard judgment calls. The analysts move up the value chain.
Trained on the scorecards a live QA team actually used.
Most QA models learn from a textbook framework or a public dataset. QEval®'s experts were trained inside a working contact center, on the rubrics a real QA team ran in operations, against the compliance edges that real auditors flagged. The difference is not academic. It is the gap between a score that looks reasonable and a score a supervisor will stand behind in a dispute.
Verint is a software company. NICE is a software company. CallMiner is a software company. ETSLabs is an operator that runs 4,000+ agents and built a product on top of its own floor.
Auto QA is Layer 1. It feeds the other five.
A QA score is the floor, not the ceiling. The same pass that grades quality and compliance generates the data for customer, revenue, operational, training, and strategic intelligence. A platform that stops at Layer 1 captures a fraction of the value. The anonymized Fortune 500 automotive program proved it: 82% of the return came from Layers 2 through 6.
Quality and Compliance
Auto QA, violation detection, audit-ready scorecards.
Customer Intelligence
CSAT prediction, sentiment trajectory, churn risk.
Revenue Intelligence
Sales behavior, conversion signals, missed revenue.
Operational Intelligence
Handle time drivers, transfer patterns, capacity.
Training Intelligence
Skill gaps, coaching queues, the HI Model protocol.
Strategic Intelligence
Cross-LOB trends, executive dashboards, planning.
A Fortune 500 automotive enterprise replaced a keyword QA engine and recovered $6.5M in a year.
Five brands, 1,200 agents, six months to full value. The old engine matched keywords with no contextual understanding and no change-management framework. Auto QA delivered the Layer 1 savings, and the layers above it delivered the rest.
Before you decide.
We already QA every interaction with our CCaaS tool. Why add this?
Full ingestion has been table stakes since 2024. The collapse happens after ingestion: most built-in QA modules sample, then extrapolate, and the accuracy is never stated. QEval® scores every conversation at a 94%+ contractual accuracy SLA with a dedicated expert on each dimension. The question is not whether you cover the interactions. It is whether the score is right enough to act on, and whether you can hold a vendor to that number.
How is 94%+ accuracy different from the "near 100%" other vendors claim?
It is contractual. Competitors publish adjectives ("near 100%," "precision accuracy"); nobody else puts a number in the master agreement. QEval®'s 94%+ accuracy, 98%+ compliance accuracy, and 95%+ violation recall are written into the contract and hold across compliance, empathy, resolution, and brand voice at the same time, not on a single benchmark task. If we miss the SLA, that is a contractual matter, not a marketing footnote.
What are "operators," and are they just another AI agent to manage?
No. The AI agents QEval® scores (Sierra, Decagon, Agentforce and others) talk to your customers. Operators do the opposite: they do the QA manager's back-office work. You assign a task, like the weekly report or a re-audit against a new policy, and the operator returns it. Each one is bound to your scorecard and cites the scored conversations behind its answer, and you can run many at once instead of working through them by hand. They use the platform capabilities you already have (summarization, reporting, analytics, compliance monitoring, coaching generation), packaged as work you can delegate.
Does automating QA mean cutting our QA team?
It has not played out that way. In production, QA analyst productivity rises about 65%, and the time is redirected to calibration, coaching, and judgment calls a model should not make alone. In the Fortune 500 automotive program, 11 FTEs were retasked rather than removed. You get capacity back, and your most experienced people stop spending the week on a sample.
Four numbers no peer publishes.
Bring a transcript. We will score it.
In 30 minutes we will score a real call from your floor against your scorecard, show you what your current QA program missed last week, and dispatch an operator to build the report live.