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Conversation quality and coaching

AI Quality Management

Every conversation gets the same fifteen dimensions. AI-handled or human-handled, voice or chat. There is no sample. The scores come out the other end as a coaching plan with named conversations attached, rather than a spreadsheet nobody opens.

  • 15 dimensions, every interaction
  • Scored live, turn by turn
  • Coaching plans per agent
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15 Evaluation dimensions
100% Interactions scored
16+ Regional compliance rule packs
100% Interactions scored, not sampled

The one that reached the regulator was in the other 98%

The rubric runs turn by turn while the conversation is still open, then again across the whole thing at wrap-up. What lands on the scorecard is evidence rather than a bare number — the disclosure timestamped at 00:12, the recap skipped at the handover, the dip at 02:10 that recovered on its own. A team lead can coach from that. A bare number out of five is something they end up defending instead.

Score every conversation, not two percent of them

Manual QA pulls a handful of calls, scores them, then argues afterwards about whether the handful was representative. That argument isn't winnable. Run one rubric across every interaction and it stops being a sample at all, which also means the call somebody asks about next Tuesday was already graded on the day it happened.

By the numbers

Few Facts - at a glance

  • 15

    evaluation dimensions, scored per turn and per conversation

  • 100%

    of interactions scored — AI-handled and human-handled, voice and chat

  • Auto

    coaching plans, generated per agent from the dimensions they are actually weak on

The process

How It Works

The four stages, end to end

Nothing gets uploaded. Scoring runs against the transcripts the platform already holds, so there is no second recording pipeline to keep alive alongside the one you have. The rubric is applied turn by turn while the conversation is live, then again across the whole conversation at wrap-up. Calibration is what keeps the rubric worth trusting, and coaching is the only reason any of the scores exist.

  1. Set the rubric

    Start from the fifteen standard dimensions and decide what each one is worth to you. Attach the compliance rule packs that apply to your jurisdiction and industry, along with the disclosures and prohibited phrases your script requires. Most teams set the weights once and leave them.

  2. Score every turn

    Each interaction is scored as it goes, then again across the whole conversation at wrap-up. AI-handled and human-handled go through the identical rubric. That is what makes the two comparable.

  3. Calibrate the evaluators

    Run a blind session where your QA team scores the same conversations without seeing each other. The variance report shows which dimensions the rubric is still vague about. Fix the wording first.

  4. Coach and re-check

    Each agent gets a plan built from the dimensions they score lowest on, with the conversations attached. Then you wait a month. Next month's scorecard shows whether the coaching moved the dimension it was aimed at, or whether the plan was aimed at the wrong thing in the first place.

Features

Every capability you need in one module

1. Fifteen dimensions, every interaction

The same rubric runs against the AI agent and against the human who took the handover, on voice and on chat. One scale, so a comparison between them means something.

Accuracy, brevity, closing effectiveness, coherence, compliance, cultural sensitivity, empathy, engagement, handoff quality, personalisation, politeness, resolution, safety, sales effectiveness and tool usage. Fifteen. Each one is scored per turn and again across the whole conversation, for AI agents and human agents alike. It matters most at the handover, where the AI half and the human half of a single call sit side by side on one scorecard.

  • Accuracy
  • Brevity
  • Closing effectiveness
  • Coherence
  • Compliance, checked against whichever rule packs you switched on
  • Cultural sensitivity
  • Empathy, the one evaluators argue about
  • Engagement
  • Handoff quality
  • Personalisation
  • Politeness
  • Resolution
  • Safety
  • Sales effectiveness
  • Tool usage, including the tool the agent should have called and did not

See what the human agent receives on handover

2. Script and compliance adherence

A compliance failure is rarely a bad conversation. It is usually a good one with a single line missing, which is exactly the thing a two percent sample walks straight past.

Four checks, run on every conversation: was the required disclosure made, was consent captured, was the mandatory question asked, was a prohibited phrase used. Behind them sit 16+ regional rule packs carrying the obligations for your jurisdiction and industry, so a collections script and a clinical intake script are not measured against the same list. A missing disclosure surfaces on the day. Not at the next audit.

  • Was the required disclosure made?
  • Was consent captured, and at which second of the call?
  • Was the mandatory question asked?
  • Was a prohibited phrase used?
GDPR HIPAA CCPA TCPA PCI-DSS DPDP FINRA EU AI Act and more

See the AI governance guardrails and audit trail

3. Calibration that keeps evaluators honest

A quality programme where two evaluators score the same call three points apart is not a quality programme. Calibration makes that disagreement visible and quantified.

Blind sessions. Your evaluators score the same interactions without seeing each other's results, and the variance report afterwards shows where the QA team quietly disagrees. Compliance and accuracy usually come back near zero. Empathy and closing effectiveness come back wide. That spread is the point of the session: it says the rubric wording is loose, and it says so before anyone gets coached on a number the rubric cannot defend.

  • Blind — scores stay hidden until the session closes
  • Variance per dimension
  • The disagreement points at the rubric, not at a person, which is the useful part
Illustrative interface. Evaluator names, scores and variances are sample data, not a performance claim.

4. Coaching plans, generated

A score is only worth collecting if something happens next. The plan names the dimension, the agent and the conversations that evidence it.

From the scores, OptiML writes a plan per agent. Two or three focus dimensions, the behaviour to change under each, plus the exact conversations that evidence it. One focus reads: recap the customer's issue before you transfer. Underneath it, the four warm transfers where the receiving agent restarted the diagnosis anyway. The team lead opens conversation 8841 at 03:12 and plays the bit that matters. Nobody has to reconstruct it from memory.

  • Per-agent, aimed at the dimensions they score lowest on
  • Evidence conversations attached to every point
  • Strengths listed as well, so it doesn't read as a charge sheet
  • Next month the scorecard says whether it worked

See agent scorecards in Analytics & Reporting

Illustrative interface. Names, scores and conversation references are sample data, not a performance claim.

5. Sentiment trajectory and hallucination detection

Where a conversation went wrong matters more than where it ended. A call that recovers from a bad opening is not the same call as one that started fine and fell apart at the handover.

Sentiment is tracked as a line across the conversation rather than a label stuck on the end, so a dip at 02:10 that recovered reads nothing like a slow slide from turn four onwards. Separately, ungrounded claims in AI answers are flagged for review. Both feed the quality score. Both land in the supervisor's live view while there is still a call running.

  • Sentiment across the whole conversation, not only its ending
  • Ungrounded claims in AI answers flagged
  • Both feed the score
  • Both land in the supervisor's live view, where somebody can still act on them

See the groundedness gate that comes first

6. Live scoring, not a report next week

Retrospective quality tells you what went wrong last week. Per-turn scoring tells the supervisor what is going wrong now, while there is still a conversation to save.

Per-turn scores go to the supervisor's quality panel while the conversation is still open, so a score that slides on turn six is visible on turn seven rather than in a report next Tuesday. Listen in. Whisper a correction, or take the call over. All three are one click away on the same panel.

  • Scored per turn, not only at wrap-up
  • Pushed live to the supervisor quality panel
  • Listen, whisper or take over, while there is still a call to save

See listen, whisper and takeover in Supervisor Console

Use Cases

Where AI Quality Management delivers value

Script and compliance adherence

A dispute that came down to one missing sentence

A collections agency (illustrative)

Scenario

A customer disputes the terms of a payment arrangement and the agency has to show what was actually said. Nobody pulls recordings. The QA lead opens the scored interaction: mandatory disclosure at 00:12, consent at 00:41, prohibited-phrasing check clean.

Outcome

The evidence is a link, not an afternoon. And the same four checks have already run on every other call the agency took that month, so the next dispute costs about as much time as this one did.

Blind calibration and variance analysis

Two evaluators, three points apart on the same call

A 400-seat BPO (illustrative)

Scenario

Team leads keep landing three points apart on empathy, and agents have started treating QA as a lottery. A blind session puts five evaluators on the same eight conversations, results hidden until it closes. Variance comes back near zero on compliance and accuracy, and wide on empathy and closing effectiveness.

Outcome

The cause is one vague descriptor in the empathy row, not a difference of opinion between five people. The wording is rewritten before the next coaching cycle runs on it.

Per-turn scoring in real time

A supervisor steps in before the booking is lost

A travel booking centre (illustrative)

Scenario

A rebooking call opens badly. Sentiment dips two minutes in, and the handoff-quality score drops when the AI agent transfers without recapping the issue, leaving the human agent to start the diagnosis over. It shows up flagged on the supervisor quality panel while it is still open.

Outcome

The supervisor whispers the recap to the agent mid-call. The alternative was reading about the transfer in next week's quality report, by which point the customer has booked somewhere else.

At a glance

Specification

The numbers and limits, without the sales copy

Specification for AI Quality Management
Specification Detail
Dimensions 15 — accuracy brevity closing coherence compliance cultural sensitivity empathy engagement handoff personalisation politeness resolution safety sales tool usage
Coverage 100% of interactions, AI-handled and human-handled, voice and chat
Scoring Per turn while the conversation is open, then per conversation at wrap-up
Source data Transcripts the platform already stores. No upload, no second recording pipeline
Compliance Script adherence, consent capture with timestamp, 16+ regional rule packs (GDPR, HIPAA, CCPA, TCPA, PCI-DSS, DPDP, FINRA, EU AI Act)
Calibration Blind scoring sessions, inter-evaluator variance per dimension, session sigma
Coaching Per-agent plans generated from the lowest-scoring dimensions, with evidence conversations attached
Detection Sentiment trajectory across the conversation, ungrounded-claim flagging on AI answers
Real-time Per-turn scores pushed to the supervisor quality panel mid-conversation
Disputes Per-dimension challenge, held against the conversation with its evidence
Residency Runs inside your tenant, in the region chosen at deployment
FAQ

Questions,
answered

What teams ask us before they roll out AI Quality Management — how it works, what it needs from your side, and what happens when it gets something wrong

Still not sure?

Talk to a specialist and get a straight answer.

Ask our team

No upload step. The conversations OptiML handles itself are scored as they happen. For the ones your human agents handle, Quality Management works from the transcript, which the platform produces from the call recording on voice and already holds on chat. There is no separate recording pipeline to run alongside the one you have.

The rubric is editable. Quality Management ships the fifteen dimensions as a default because they are what most quality programmes end up building for themselves anyway, and you set what each one is worth. The compliance rule packs are chosen per jurisdiction and industry rather than applied wholesale. Just know that scores either side of a rubric edit aren't comparable, which is why calibration sessions always run against a fixed version.

Any score in Quality Management can be disputed on a specific dimension. An agent or a team lead challenges it, and the dispute stays attached to the conversation with the evidence next to it, so the correction is auditable rather than a corridor conversation. A run of disputes on one dimension is a rubric problem, not an agent problem. The calibration variance report is where that shows up.

Both, on one rubric and one scale. Quality Management scores AI-handled and human-handled conversations identically, which is the only way a comparison between them means anything. When a conversation is handed from the AI agent to a person you can see which half scored what, and handoff quality is itself one of the fifteen dimensions, so a transfer with no recap is scored as the problem it is.

It does not. Quality Management scores inside your OptiML tenant, in the region you chose when the platform was deployed, and it reads the same stored transcripts the rest of the platform already uses rather than making a second copy for QA. Nothing is shipped out to an external quality vendor. Retention and access follow the policies already set on your recordings.

Keep exploring

Additional supporting modules

Native modules that share the same record, knowledge and governance

Platform module

Supervisor Console

Listen, whisper and takeover on the live floor, which is where a per-turn score turns into an intervention.

Explore
Platform module

Analytics & Reporting

76 reports including agent scorecards and quality trends, exportable six ways.

Explore
Platform module

AI Governance & Guardrails

Eval-gated prompt promotion, so a change that scores worse than the incumbent doesn't ship.

Explore
Connected solutions

Where AI Quality Management is used

The Solutions pages that lean on this module, and what it looks like once it is configured for a particular floor, job title or job to be done.

7 solutions built on this module

By industry

Sector floors that run on it

2

By role

Job titles that answer for it

3

By use case

Jobs it is put to work on

2
Browse every solution
Talk to a specialist

Bring a call your team has already scored

Send us one recording your evaluators have already graded. We will run it through the fifteen dimensions live on the call, and you will see where the machine agrees with your evaluator and, more usefully, where it does not.

  • 30 minutes
  • Your own conversation, scored live
  • Bring the evaluator who graded it

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