Start with a pilot. Scale as your AI matures.
Most clients begin with a pilot, then expand into release-cycle testing or continuous QA as their conversational AI moves toward production and scale. Pricing is scoped per engagement — we'll size it to your agent and goal.
Pilot
- › One agent from any service line, scored on its own rubric
- › Structured human evaluation across defined scenarios
- › Scored failure map & scorecard
- › Prioritized fixes and a retest plan
Release-Cycle Testing
- › Regression testing before major releases
- › Consistent rubric across versions
- › Version-over-version trend reports
- › Catch failures before they reach production
Monthly QA Retainer
- › Dedicated monthly evaluation capacity
- › Continuous failure discovery on production
- › Fresh scenarios and adversarial passes
- › Standing engineering reports
Pricing is scoped to the agent, evaluation depth, scenario count, and engagement model — so we don't publish fixed rates. Most clients start with a pilot before moving to release-cycle testing or a retainer.
The same backbone, whichever model you pick.
Every model runs across the full service catalog — voice, sales, support, contact center, enterprise — each scored on the rubric built for that interaction type. The engagement decides cadence; the method underneath it does not change.
Per-service scoring
Each agent is scored on the rubric built for its interaction type. There is no universal scorecard.
Independent 3-layer QA
Execution, validation and a final evaluation authority stand between every conversation and your report.
Conversation Intelligence Report
The failure map, scorecard, severity-ranked findings and prioritized fixes — one engineer-ready document.
Supporting evidence
Full call recordings and transcripts from every evaluated interaction, included with the deliverable.
Most teams start with a pilot.
It's the fastest way to see how your agent performs under independent human evaluation — then decide whether release-cycle testing or continuous QA makes sense.