AI Integration
AI features inside your own product: search and answers over your documents, extraction from invoices and forms, and assistants built into the screens your users already work in. The engineering that matters is evaluation, cost control and graceful failure — a demo is easy, a feature you can leave running is not.
Top services
Search over your documents (RAG)
Answers drawn from your manuals, policies or records, with the source cited.
AI assistants inside your app
An assistant in your product's own screens that reads and acts on the user's data, within limits you set.
Semantic search
Search that finds what users mean, not only the words they typed.
Summaries & drafting features
Summaries of long records and first drafts of replies, notes or descriptions, inside your product.
OpenAI & Claude API integration
Models called from your backend with the prompts, caching and fallbacks a production feature needs.
LLM testing & cost control
Test sets that score every prompt or model change, and budgets that keep the bill predictable.
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 an AI 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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