LLM evaluation services
We build the evaluation layer most AI features ship without: eval harnesses tied to your real traffic, shadow-mode testing for new models, and regression suites that catch quality drift before your users report it. If you can't answer 'did that prompt change make it better', this is the missing piece.
No sales script. You talk to the engineers who'd build it.
Our team works a shifted day so you get real-time standups and same-day turnarounds in your time zone, not next-morning replies.
Every line of code, model weight, and prompt is yours from day one. NDAs and clean IP assignment are standard, not an upsell.
You work directly with the engineers building your system. No account managers sitting between you and the people writing code.
We move from scoping to a working system in production in weeks. Most engagements ship something usable inside the first month.
What we build
Concrete systems we ship, tuned to your data and your stack.
Evals from real traffic
Built from your production distribution, not the cases you thought you'd have when you wrote the feature.
Shadow-mode testing
Route a copy of live traffic to a candidate model, measure, and decide from evidence rather than launch posts.
The four metrics
Cost per completed task, retry rate, p95 latency, and failure mode. Not token price and not benchmark averages.
Regression suites in CI
Prompt and model changes run against the suite, so quality drift fails a build instead of reaching users.
How we work
Scope & evals
We pin down what success means and build the evaluation set before writing the feature, so quality is measured, not guessed.
Build in the open
Weekly demos against real data. You see progress every week and can change direction before it gets expensive.
Ship & instrument
We deploy with logging, cost tracking, and guardrails in place, then tune against production traffic.
Hand off or stay
Take the keys with full docs, or keep us on for iteration. Either way you're never locked in.
Questions, answered
We already have benchmarks. Isn't that enough?
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Public benchmarks measure other people's distributions. They're a signal for what to test, never a substitute for testing. The gap between a benchmark table and your traffic is where production failures live.
What is shadow mode?
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You route a copy of real requests to a candidate model without serving its output, then compare. It's the cheapest honest way to evaluate a model, and it costs single-digit dollars a day for most workloads.
How do you measure quality on subjective output?
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A mix of graded rubrics, structural checks your validator can run, and human review on a sampled slice. The goal is a number that moves in the right direction, not a perfect one.
Can you evaluate a system we didn't build?
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Yes, and it's a common starting point. An eval harness on an inherited system is usually the fastest way to find out what's actually wrong before anyone commits to a rebuild.
Let's scope your build.
Tell us what you're trying to ship. We'll tell you honestly whether AI is the right tool and what it would take.
Start the conversation