AI / ML Integration
Retrieval-augmented search, LLM-backed product features and custom ML pipelines. The engineering that matters is evaluation, cost control and graceful failure — a demo is easy, a feature you can leave running is not.
What this covers
Retrieval over your own data
Chunking, embeddings and vector search tuned to your corpus, with citations so an answer can be checked rather than trusted blindly.
Evaluation before rollout
A test set and a scoring method, so a prompt or model change is a measured decision instead of a vibe.
Cost and latency as constraints
Caching, model routing and token budgets designed in, because per-request pricing turns a popular feature into a bill.
Failing safely
Timeouts, fallbacks and clear boundaries on what the model is allowed to decide. The feature should degrade, not produce confident nonsense.
Have a ai / ml integration project?
Tell us the problem and the constraints you are working within. We will come back with how we would approach it.
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