Know exactly where your AI stands before you ship.
We run a structured 64-case evaluation across 6 quality dimensions and deliver a scored baseline report — with your test suite configured and documented so your team can run it on every future deployment.
6
Quality Dimensions
64
Pre-built Test Cases
48hr
Report Turnaround
100%
Deterministic Verdicts
Why this matters
Most teams deploy AI changes and hope quality holds. Without a systematic eval suite, regressions are invisible until users complain. A single prompt change can silently break refusal behaviour, factual accuracy, or scope adherence — and you won't know until it's in production.
How We Do It
A structured process, every engagement.
Define quality metrics
We identify which dimensions matter most for your use case and domain, and define pass/fail thresholds.
Select or build your test suite
Choose from 64 pre-built cases or build custom cases against your actual user queries and expected outputs.
Run and score the baseline
Full suite run against your current model and prompt — every dimension scored, every verdict documented with evidence.
Deliver findings and test suite
HTML + PDF report with your scored baseline. Your test suite is documented and yours to run on every future change.
Optional: CI/CD integration guidance
We show your team how to wire the eval suite into your deployment pipeline so it gates every future release.
Powered by our LLM Quality Evaluator
- 6 quality dimensions: Factual Accuracy, Instruction Following, Refusal Precision, Consistency, Hallucination Detection, Scope Adherence
- 64 pre-built test cases across Healthcare, Fintech, Legal, Coding, Education, and Customer Support domains
- Pattern-based PASS / PARTIAL / FAIL — deterministic, not AI-judges-AI
- Before/after comparison runs to validate any change
- HTML + PDF Assessment Reports ready for client or stakeholder delivery
What You Get
Tangible deliverables, not slide decks.
Who It's For
Built for teams where AI reliability is non-negotiable.
Pre-launch teams
Establish a quality baseline before going live — know exactly where your model stands before users depend on it.
Teams experiencing quality issues
Diagnose which dimension is failing — hallucination, scope drift, refusal gaps — with evidence, not guesses.
Teams iterating on prompts or models
Validate that changes improve quality without causing regressions elsewhere before every deployment.
Ready to get started?
Book a free 30-minute AI Reliability Assessment. We'll review your stack, identify your highest-risk failure modes, and show you exactly what to fix first.
Book a Free Scoping Call →