        Continuous AI QA — After Launch
      

AI needs QA after launch.

Your AI agent is talking to customers right now. Someone should be checking its work — every conversation, human or AI, scored against what actually matters.

See the QA Scorecard Review your QA model →

QA SCORECARD — CALL #2,841 REVIEW

Original intent identifiedPASS

Answer from approved sourcesPASS

Tone — clear, calm, on-brandPASS

Handoff — context transferredFLAG

Resolution — intent satisfiedFAIL

GAP LOGGED → FIX PUSHED → RE-SCORED Illustrative

Most teams QA 1–3% of conversations. AI made that a liability.

When humans handled every call, sampling was a compromise you could live with. Now an AI agent can handle thousands of conversations a week — consistently right, or consistently wrong. A 2% sample won't catch the difference.

AI without QA is a new risk layer. Continuous AI QA scores every conversation — and turns what it finds into fixes, not just reports.

THE SCORECARD

Ten lenses on every conversation.

Human and AI, scored the same way.

Original intent — did we identify why the customer actually reached out?

Resolution quality — answered, booked, routed, or completed to the customer's satisfaction?

Failure state — when AI couldn't resolve it, did the handoff work cleanly?

Repeat-contact risk — is this customer likely to call, text, or escalate again?

Source accuracy — did the answer come from approved, current material?

Handoff completeness — did the right person get the summary, context, next step, urgency?

Tone & trust — did it feel clear, calm, respectful, and on-brand?

Operational defect — did it reveal a broken process, policy, or training gap?

Coaching opportunity — what should an agent, manager, or team learn from it?

Expansion signal — which repeatable workflow should be fixed next?

[ DOWNLOAD ] The full AI QA Scorecard rubric — scoring criteria for all ten lenses.

Get the scorecard →

SCORES BECOME FIXES

QA that ends in a push, not a PDF.

Every flagged conversation traces to a root cause. Every fix is approved by a human, pushed live, and re-scored.

Score

Every conversation, against the ten lenses.

Find the gap

Root cause: knowledge, instruction, execution, or policy.

Fix

A proposed change — reviewed and approved by a human.

Push

The fix goes live, and the change is logged.

05PROVED

Brief

The result lands in your executive briefing — with the metric that moved.

The reporting rhythm

ARTIFACTS, NOT DASHBOARDS

DAY 7

Stabilization report

First week live: what held, what leaked, what changed.

DAY 14

Stabilization report

Trend check against the baseline locked at kickoff.

DAY 30

Closeout report

Stabilization ends; steady-state QA begins.

MONTHLY

Executive briefing

Wins, risks, defect themes, and the next workflow — one page.

QUARTERLY

Business review

The proof plan, reviewed against business outcomes.

"Every review ends in an artifact an operator can read in ten minutes and act on the same day."

FOR REGULATED INDUSTRIES

Redaction-first

Personal information is redacted before any analysis touches a transcript.

Your rules, enforced

What your agent must never do — quote a guaranteed rate, read results aloud — becomes an active rule scored on every call.

A defensible trail

Every flag carries its reasoning and evidence — built for healthcare and finance review.

[ PROOF NEEDED ]

AI QA findings from a live workflow

Real scorecard results from a production agent will sit here once the wording is approved.

Who's checking your AI's work?

20 minutes. Your data. No pitch deck.

Review Your QA Model Start with the AI Audit