Your AI works in the demo. Does it work on Tuesday?
Systematic evals for LLM systems: reliability under real inputs, regression detection on every change, and edge cases found before your users find them.
This is for you if...
Perfect fit
- Your LLM feature behaves differently every week and nobody knows why
- Prompt changes ship without any measurement of what got better or worse
- Support tickets are your current eval suite
- A model upgrade broke production and you found out from users
- You need documented test evidence for stakeholders or regulators
Not the best fit
- You have no AI system in production or near it yet
- You expect 100% deterministic behavior from a probabilistic system
Concrete Outcomes
No vague promises. Here's what actually changes.
Eval Suite on Real Data
Test sets built from your actual traffic and failure modes — graded automatically, reviewed where it counts.
Golden datasets, automated grading, human review loop
Regressions Caught in CI
Every prompt, model, or pipeline change measured before it ships, not after it breaks.
CI gates, score thresholds, per-change diff reports
Numbers You Can Show
Reliability metrics that survive a hard question from your board, your customer, or your auditor.
Accuracy/safety dashboards, documented methodology, trend reports
How We Work Together
A clear, step-by-step approach so you know exactly what to expect.
Map failure modes
Where your system actually fails: hallucination, formatting, tool misuse, tone, latency — ranked by user impact.
Failure-mode map
1 workshop, sample traffic
Build the eval suite
Datasets from real traffic, graders (LLM and rule-based), and thresholds that mean something.
Datasets, graders, thresholds
Data access, review loop
Wire into CI
Evals run on every change; regressions block the merge instead of reaching production.
CI gates live
Pipeline access
Iterate on evidence
Monthly review of scores vs. real-world outcomes, tightening the suite as the product evolves.
Monthly eval report
1 review call per month