AI call scoring that shows its work.
Most AI call scoring gives you a number. Stratyfix shows you the sentence.
AI call scoring uses large language models and speech-to-text to evaluate a sales or customer call against a rubric — breaking the conversation into measurable behaviors (discovery, objection handling, next steps) and scoring each one, instead of leaving a manager to grade a handful of calls from memory. Explainable AI call scoring goes one step further: every score is tied to the exact moment in the transcript that justifies it, with the reasoning shown — a grade you can read, check, and coach from, not a black box you're asked to trust.
That difference is the whole point. A score you can't see the basis for is worse than no score — because people act on it.
AI call scoring runs a consistent pipeline: it captures and transcribes the call, separates who said what, then evaluates the transcript against a rubric of defined behaviors — assigning each behavior a score with the evidence that drove it, and rolling those up into an overall result. The rubric is the heart of it. A good sales-call rubric measures behaviors, not vibes:
Discovery quality
Did the rep ask open questions and uncover real business pain? Gong's analysis of 326,000 calls put the sweet spot around 11–14 discovery questions — more isn't better.
Qualification coverage
Whichever framework you run — MEDDIC / MEDDPICC, BANT, SPICED, SPIN. Was the economic buyer confirmed? The decision process surfaced?
Talk-to-listen ratio
Top performers land near 43% talk / 57% listen (Gong) — but a good rubric weights this by call stage, not as a flat target. Discovery rewards listening; negotiation rewards concision.
Objection handling
Were objections explored and reframed, or deflected? Was price handled after value was established?
Next-step securing
Was a clear, mutually-agreed, calendared next step set? Often the near pass/fail line of the whole call.
Stratyfix scores these on a five-band behavioral scale, and records the exact basis for every result — the criteria, the evidence, and the reasoning — so a score from today can be reproduced and explained months later.
When an AI hands a rep a “6 out of 10” with no reasoning, two things break: the rep doesn't trust it, and the manager can't coach from it. This isn't hypothetical — the category leader's own documentation concedes that its AI-filled scorecards come with no per-score rationale or evidence (a human just accepts or overrides the number), and that the model “has a bias to answer ‘yes.’”
“AI doesn't understand nuance. I've seen this type of system lower people's scores, rating it as a poor interaction because the client was mad even if the person on the phone handled it well.”
— a contact-center rep, r/callcentres
And the moment a number becomes the target, people optimize the number instead of the customer — Goodhart's Law: “when a measure becomes a target, it ceases to be a good measure.” The fix isn't a better number. It's a number that shows its work.
Explainable AI call scoring means every score arrives with the evidence and reasoning behind it — you can trace any grade back to the exact words on the call. NIST defines explainable AI as output that “delivers accompanying evidence or reasons.” That's the standard Stratyfix is built to:
Evidence, verified
Every behavior score points to a verbatim transcript quote — and each quote is checked against the actual transcript, so a hallucinated citation gets flagged, not trusted.
Reasoning, written
A plain-language rationale for every score — so you can see exactly what drove it, and where and why anything was adjusted.
Anchored in objective measures
Objective measures the call itself produces — like talk-to-listen ratio and how long the rep talks without pausing — anchor the score, so a grade can't contradict what measurably happened. One strong moment can't carry an otherwise weak call.
No inflated scores
The system won't hand out a high score it can't back with evidence — and any adjustment to a score is shown, in plain sight, with the reason for it.
Reproducible and logged
Every result records the rubric and the evidence behind it, so the same call grades the same way. Changes are written to a permission-locked, append-only audit log — a disputed grade can be reconstructed and defended, not argued from memory.
↳ No competitor on page one of “AI call scoring” documents this depth.
You should be able to answer it in one screen. For any score, Stratyfix shows the criterion, the exact transcript quote, the reasoning, and the band — the receipts, not a verdict.
Strong layered discovery: the rep deferred pricing to surface a quantified business pain (two lost accounts), then probed implication with the end-user — a Need-payoff move. Held at Proficient, not Expert, because the economic buyer's metrics were never confirmed.quote verified
Conversation intelligence (Gong, Chorus) is built to analyze deals and pipeline; scoring is a side feature, and the documented weak spot. Explainable call scoring is built to grade the conversation — and to show why.
| Conversation intelligence | Explainable AI call scoring (Stratyfix) | |
|---|---|---|
| Built for | Deal & pipeline visibility, forecasting | Grading the call & coaching the rep |
| The score | A number, often with no per-score rationale | Every score tied to the transcript moment |
| If it's wrong | Human accepts or overrides the number | The evidence is shown — you check it, not it |
| Coverage | Recorded calls | Recorded calls & live practice — one rubric |
| Reproducible | — | Rubric + evidence logged, append-only |
The same evidence-tied score works wherever a conversation needs to get better — across the whole revenue and CX org.
Onboarding & ramp
New reps see exactly which behaviors close the gap to quota — with the evidence, not a vague “improve discovery.”
Customer success
Score renewal and QBR calls on the behaviors that retain — flagged before the relationship slips.
Support QA
Move beyond gut-feel call reviews. Score every interaction, escalate the ones that actually need a human.
Sales leadership
See which behaviors move win rate across the team — and where coaching is actually landing, week over week.
Sales coaching
Turn every call into a drill: today's missed moment becomes tonight's rehearsal, graded the same way.
Scoring people's work is high-stakes. The AI proposes; a human decides.
Human-in-the-loop by default
The coach proposes; high-stakes actions are never auto-executed.
Consent-first
Recording requires stored consent; the live coaching bot discloses it's AI to the room.
Auditable
Append-only, permission-locked decision logs you can export.
Enterprise security
SSO / SAML, SCIM, RBAC, tenant isolation, encryption of secrets, configurable retention.
What is AI call scoring?
How does AI score a sales call?
Is AI call scoring accurate?
Why did I get this score?
Is AI call scoring fair?
How is this different from Gong or Chorus?
Can AI call scoring be gamed?
What about call-recording consent?
Does it work on real calls or just practice?
Watch it grade a real call — and show its work.
Bring one of your own calls. In 30 minutes you'll see every score tied to the exact transcript moment behind it — the receipts, not a black box.