RAG system
For grounding model output in your own documents and data.
- Document ingestion pipeline
- Embeddings + vector store
- Retrieval + reranking
- Evals harness with golden sets
- Admin interface for sources
- 3–5 week delivery
No hourly billing games, no scope-creep invoices, no line items with names you need to Google. You see a number, you agree, and we ship what we quoted. If we underestimate, that cost lands on us, not on your runway.
For adding one well-scoped AI feature to an existing product.
For grounding model output in your own documents and data.
For multi-step agentic workflows with tool use and monitoring.
Need something between these? Get in touch
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New scope gets a new fixed quote, never an uncapped hourly line item. Small tweaks stay inside the original number. Bigger changes become an explicit agreement you sign off on, so nothing catches you off guard.
Yes. Larger engagements split into milestone payments, typically 40% at kickoff, 40% at demo, and 20% at launch. We'll shape a rhythm that works with your cash flow.
A one-page doc with scope, deliverables, timeline, and a fixed total. No hourly meters, no 'additional expenses', no fine print. Sign it, and that's the number.
We're provider-agnostic, working with Anthropic, OpenAI, open-weight models via Ollama / Bedrock, and local inference. The right choice depends on cost, latency, and data-residency needs. We'll benchmark a few on your actual workload before committing.
You do. Inference costs are billed to your accounts directly (Anthropic, OpenAI, etc.). We instrument cost and usage from day one so nothing is a surprise at month-end.
Every AI build ships with an evals harness: golden-set test cases, automated regression runs on prompt or model changes, and production sampling. Hallucinations don't drop to zero, but with this in place you can measure and track them.