A generative AI development company past the wrapper phase
We are a generative AI development company that works on the part after the prototype. Retrieval that returns the right document, evaluation that tells you whether quality moved, and cost per request that does not surprise anyone in month three.
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.
RAG that actually retrieves
Most failing RAG systems have a retrieval problem, not a model problem. We fix chunking, hybrid search, and reranking before touching prompts.
In-product copilots
AI features inside your product that take real actions, with the streaming, error handling, and permission checks a real feature needs.
Evaluation harnesses
A test suite for your AI, built from real cases, so you can tell whether a change helped instead of arguing about vibes.
Cost and latency engineering
Model routing, caching, and effort control so you spend on the requests where it changes the answer.
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
Our prototype works but breaks with real users. Why?
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Nearly always retrieval quality, unhandled input variety, or missing evaluation. The prototype was built against clean inputs, and production is not clean. That gap is most of what we do.
Should we fine-tune or use RAG?
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RAG for knowledge that changes, fine-tuning for behaviour and format that does not, long context for a bounded set of documents. Most teams asking about fine-tuning have a retrieval problem.
How do you keep costs under control?
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Measure per completed task rather than per call, route the easy work to smaller models, cache aggressively, and control reasoning effort per step. Usually there is a 40 to 60% reduction available without a quality change.
Can you work with open weight models?
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Yes, including self-hosted deployments with vLLM in your own infrastructure. We will be honest about when the operational cost of self-hosting outweighs the API bill, which is more often than people expect.
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.