Generative AI services priced against an outcome
Our generative AI services cover the whole lifecycle, and the two most requested are not new builds. They are cutting an inference bill that grew faster than usage, and building the evaluation harness that should have existed before the first launch.
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.
Feature build
New LLM features shipped into your product, in your stack, as PRs your team reviews.
Cost optimisation
Routing, caching, and effort control. In most codebases we look at there is 40 to 60% available without a quality change.
Evaluation harnesses
Real cases, scored automatically, run on every change, kept in your repository rather than a vendor's tooling.
Model migration
Provider or version changes validated in shadow mode against your evals, so you find the regression before your users do.
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
How do you find cost savings?
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Usually four places: context that grows every loop iteration, retries billed twice, reasoning effort spent where it changes nothing, and a large model doing work a small one handles identically.
We have no evals. Is that a problem?
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It is the most common gap and the most consequential, because without them every change is a guess. Building the first useful set from your production traces takes about a week.
Should we upgrade to the newest model?
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Run it in shadow mode against your evals for 48 hours. Benchmark scores tell you about benchmarks, and the only thing that matters is your task.
Do you offer ongoing support?
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Either handover with documentation and runbooks, or an ongoing arrangement. Quality degrades quietly as upstream systems change, so somebody needs to be sampling output monthly.
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.