An LLM development company for the work after the demo
We do LLM engineering for teams already in production: evaluation harnesses, model migrations that do not regress quality, self-hosted open weight deployments, and the cost work that makes the bill predictable.
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.
Evaluation harnesses
Real cases, scored automatically, run on every change. The thing that turns model upgrades from a gamble into a decision.
Model migration
Moving providers or versions without silently regressing quality, using shadow mode and a scored comparison rather than a vibe check.
Fine-tuning and distillation
Where a smaller tuned model beats a larger general one on your task, which is a narrower case than the market suggests.
Self-hosting with vLLM
Open weight models in your own infrastructure, with an honest cost and operations comparison against the API first.
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
Should we self-host an LLM?
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Only if data policy requires it, or your volume is high and steady enough that the GPU bill beats the API bill after you count the operations time. For most teams the honest answer is no, and we will say so.
A new model launched. Should we switch?
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Run it in shadow mode against your eval set for 48 hours and let the numbers decide. Switching on benchmark scores is how teams regress quality on the one task they care about.
How much can we realistically cut our LLM bill?
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In most codebases we look at, 40 to 60% without a quality change, mostly through routing, caching, and not spending reasoning effort where it changes nothing.
Do you build evals if we have none?
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Yes, and it is usually the first thing we do. Evaluation sets built from your real production traces, in your repository, in a format you control rather than a vendor's.
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.