Catch regressions before your users do.
We run structured evaluation pipelines against your LLM on every model change, prompt update, or deployment — scoring across 6 quality dimensions with deterministic, auditable verdicts.
6
Quality Dimensions
57+
Pre-built Test Cases
6
Domain Suites
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 57+ pre-built cases or build custom cases against your actual user queries and expected outputs.
Establish baseline scores
Run the full suite against your current model and prompt — every dimension scored, results documented.
Run on every change
Eval suite re-runs on model swaps, prompt updates, or deployment triggers. Regressions surface immediately.
Report and retest
Findings delivered as a scored Markdown report. Remediated issues are retested to confirm resolution.
Powered by our LLM Quality Evaluator
- 6 quality dimensions: Factual Accuracy, Instruction Following, Refusal Precision, Consistency, Hallucination Detection, Scope Adherence
- 57+ pre-built test cases across Healthcare, Fintech, Legal, Coding, Education, and Customer Support domains
- Pattern-based PASS / PARTIAL / FAIL — deterministic, not AI-judges-AI
- Compare-runs: side-by-side scoring before and after any change
- Markdown 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 Your Free Assessment →