Retrieval-augmented generation and LLM-powered features built for production — vector search, chunking strategy, evaluation, observability, and the guardrails that make non-deterministic systems defensible to legal, security, and finance.
Most RAG demos work. Most RAG products in production drift, hallucinate, and produce results no one is accountable for. We instrument evals, log everything, and build the human-review loop in from day one — not as a 2.0 feature.
AI features your legal, security, and CFO teams will defend — not just the engineering team.
Concrete deliverables — not adjectives. Each engagement scopes which of these are in play and what success looks like for them.
Drawn from sales calls, not SEO filler. Want a question added? Drop it in the form on this page — we update from real enquiries.
pgvector when you already have Postgres and the corpus is small-to-mid. Pinecone for managed scale. Weaviate when you want hybrid search with first-class metadata. We benchmark for the actual workload.
Citation-backed responses, retrieval gating, output schema validation, and human-review queues for high-stakes paths. Hallucination is a systems problem, not a model problem.
CI-blocking. We build eval datasets early and gate releases on regression, the same way we gate on test failures.
We architect for provider portability — usually via Vercel AI Gateway or a thin adapter — so swapping models or providers is a config change, not a rewrite.
OpenAI integrations for products that need the latest GPT model surface — function calling, structured outputs, embeddings, vision, and the Realtime API.
Claude API integrations for products where Anthropic's models earn the seat — long-context reasoning, code understanding, tool use, and computer use.
The data engineering that makes AI honest and analytics defensible — warehouses, ELT pipelines, dbt, semantic layers, and the dashboards executives actually use to decide things.
