AI features that survive real users.
LLM applications, retrieval pipelines, computer vision and the data infrastructure behind them. We build AI features the way we build the rest of the product — with evals, observability and a clear answer to what happens when the model is wrong.
Most AI demos work on the demo dataset and break on the second customer. We ship the second kind.
Production AI is an engineering discipline before it’s a model choice. Evals come before prompts, retrieval comes before fine-tuning, and observability comes before launch. We treat latency, cost and accuracy as a budget the system has to live inside — and we leave the budget meter running long after we’re gone.
Eval first, prompts second
Before we write a prompt, we write the eval set that says ‘good’ — golden examples, edge cases, the cases that already burned the team. Everything downstream is measured against it.
Build retrieval like a database
RAG isn’t a chatbot pattern, it’s a data system. We pay attention to chunking, hybrid search, freshness and the boring parts that decide whether the answer is right.
Treat the model like a vendor
Models change. Costs change. We abstract the provider, run side-by-side evals, and pick the smallest model that clears the bar — not the biggest one in the press.
Ship with guardrails
Schema-constrained outputs, structured tool calls, fall-through human paths and a clear escalation route for the cases the model shouldn’t own.
How do we know the AI feature is good enough to ship?
Which model should we use?
What about hallucinations and bad outputs?
Do we need to be on a specific cloud or vendor?
Web Platforms
Where most AI features land — the UI, the streaming response, the latency budget.
ExploreCloud & DevOps
GPU autoscaling, vector-DB ops and the bill you can actually control.
ExploreQuality Engineering
Evals are tests for non-deterministic systems. We build the test pyramid for them.
Explore