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Corei Evals · Admin

Catch AI regressions before customers do.

A continuous eval harness that runs every change to Corei against a library of real MSP scenarios. Accuracy, citation quality, tool-use correctness, and safety scored on every commit.

Eval run #284 · prompt v18
96.4% pass
✗ 6 of 24 mislabeled as incident · regression vs v17

Silent regressions

Prompt change breaks summaries; nobody notices for a week.

Black-box trust

Hard to defend AI output to a customer without trace evidence.

No ground truth

Model 'feels worse' — but there's no measurement to confirm.

Risky rollouts

New model goes live without a quantified comparison.

Capabilities

Built for the way dispatchers, leads, and admins actually work.

Scenario library

Hundreds of real MSP prompts with expected behaviors and ground truth.

Accuracy scoring

Per-scenario pass/fail with model, prompt version, and tool trace.

Regression alerts

Block prompt or model changes that degrade scores beyond threshold.

A/B prompts

Compare two prompts head-to-head across the full scenario set.

Trace explorer

Step through tool calls, intermediate thoughts, and citations.

Audit-ready reports

Export eval runs for compliance and customer assurance reviews.

How it works

A simple loop, run continuously.

01

Curate scenarios

Real ticket triage, draft reply, summary, and tool-use cases.

02

Define rubrics

Accuracy, citation quality, tool-call correctness, safety.

03

Score every change

Eval suite runs on every prompt or model bump.

04

Gate or roll back

Regressions block the change automatically.

Last 7 runs

Quality trend over time.

Run #284 · 96.4% regression
  • Hundreds of MSP scenarios
  • Per-rubric scoring
  • A/B prompt comparison
  • Tool-trace explorer
  • Auto-block on regression
  • Export for audit
Corei

Ask Corei.

Corei works inside this module with cited sources and permission checks. You choose whether it advises, pauses for approval, or runs trusted work.

Example

How did the last prompt change affect triage accuracy?

C

Triage P1/P2 accuracy held at 96.4%. P3/P4 tagging dropped 1.8 points — driven by 6 scenarios where the new prompt over-categorized 'request' tickets as 'incident'. Suggest reverting the request-handling clause.

See it in your tenant.

Book a 45–60 minute working demo with the Corei team — we'll wire this module to your live data.

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