100%
Of cases, not 1–2%
A customer's problem crosses many agents, channels and days. Whole-Case QA scores that entire journey as one connected case, not a pile of disconnected calls, and tells you whether the issue was truly resolved.
Of cases, not 1–2%
One journey
Every verdict explained
QA teams review interactions by hand, so they sample 1–2% and hope the rest are fine. And the few they do check are judged one call at a time, in isolation.
So the moment that actually decides whether a customer stays or leaves—the messy handoff between agents, the promise made on a call and dropped in an email—is exactly the moment nobody scores.
Every agent passed the customer still left.
Missed upsells and fixable failures, invisible at scale.
A missed disclosure in one channel goes undetected.
Coaching takes weeks the same mistakes repeat.
| The question | × Traditional call-by-call QA | ✓ Whole-case QA |
|---|---|---|
| WHAT’S JUDGED | ×One call, on its own | ✓The whole case every agent & channel |
| COVERAGE | ×1–2%, sampled by hand | ✓100%, scored automatically |
| HANDOFFS BETWEEN AGENTS | ×Invisible | ✓Scored—you see where it broke |
| CHANNELS | ×Voice only, usually | ✓Voice, WhatsApp & email in one journey |
| BEHIND EVERY SCORE | ×Trust the reviewer | ✓The exact moment, cited & playable |
| SPEED | ×Days to weeks | ✓Minutes after the case closes |
| FINAL SAY | ×Reviewer, alone & subjective | ✓AI scores; humans confirm or override |
The unit we score is the case × agent slice: one agent’s handling of one case. Stitch every slice back together and the whole journey reads end to end.
Takes the refund request, promises a callback within 24h.
Slice passedAnswers a chase message but never logs the promised callback.
2 findingsBlind-transfers the case to another team with no context.
Auto-failApologises, but the refund still hasn’t been processed.
1 findingCASE VERDICT
3 of 4 agents “passed” in isolation.
Each slice is graded point-by-point against a configurable rubric — built on your SOPs, not a generic template.
Some misses are unforgivable. A missed identity check, a missing disclosure or a leaked data point overrides everything else — the slice fails no matter how well the rest of it went. There's no “good” rating on a broken case.
It plugs into the systems you already run—Salesforce, Genesys, Infobip—and does the heavy lifting before a human ever looks.
Pulls resolved cases and interactions from your stack.
Salesforce · Genesys · InfobipTurns every call and chat into searchable text.
ASR · voice + chatSplits the case into one slice per agent.
case × agentGrades each check pass/fail; auto-fail zeroes the slice.
rules + QMT on any missAttaches its reasoning and the exact evidence.
one-click to sourceYour team confirms, corrects or overrides—final say stays human.
HITL · feeds back to improveNo black box. Every verdict opens with its evidence on the left and the AI’s reasoning on the right—the transcript, chat or audio, one click away.
Reviewers confirm what’s right, correct what’s wrong, and override anything. Each correction is recorded and quietly sharpens the model for next time.
Running today inside a live enterprise travel operation—evaluating voice, WhatsApp and email across multiple teams and products, every verdict human-reviewed.
Rubric checks
Cases / day
evaluable
Channels, one
case
Human final
say
Read as one journey, a case stops being a grade and becomes a map: the exact handoff that failed, the promise that slipped, the fix worth making.
Pick one real case that went sideways across a few agents and channels. We’ll show you exactly where it broke and what a resolved version looks like.