We build AI that works on your data, not the demo dataset — evaluated, observable, and cost-tuned to run in production without burning a series A on tokens.
Not a menu of buzzwords — the concrete things our team delivers on every ai development engagement.
Hybrid search, re-ranking, and evals on your corpus — not a five-line LangChain example from a blog.
pgvector, Pinecone, or Weaviate paired with Postgres so your AI respects ACLs and business rules.
We benchmark prompt, RAG, and fine-tune options before recommending one. Most of the time you do not need to fine-tune.
LangSmith, Langfuse, or custom evals wired in from day one. No "it worked in testing" surprises.
Prompt injection defences, output filters, and PII redaction built for enterprise review.
Swap GPT-4o, Claude, Gemini, or open models via a single abstraction. No vendor lock-in.
No discovery phase that never ends. Each step has a deliverable, a date, and a demo.
We look at your data, sample queries, and current pain points before promising an AI solution.
A working prototype on your data in two weeks, with an eval set and a baseline accuracy number.
Caching, streaming, cost controls, and fallback models before the first user sees it.
LangSmith or Langfuse dashboards, alerting on drift and cost, and a weekly eval review cadence.
Opinionated defaults — not a buzzword bingo card. We swap pieces when your product calls for it.
A 30-minute call. We'll talk scope, timelines, and what a realistic first release looks like. NDA signed before we start.