LLM applications and RAG systems
Retrieval-augmented generation pipelines that ground LLMs in your data with citations, audit trails, and a private deployment option.
Sound familiar?
- 01Public LLMs hallucinate on your domain and can’t cite sources.
- 02Off-the-shelf RAG misses your vocabulary, your data shape, your formats.
- 03Private deployment is a non-starter for IT — until it isn’t.
Concrete outputs.
How we run it.
Discover
Use-case scoping, data access, success metrics, eval design.
Design
Model + retrieval architecture, security boundary, UI contract.
Build
Ingest, index, integrate, test against eval harness.
Operate
Production deploy, drift monitoring, retrain cadence.
What pairs well with this.
- AI / GenAI Engineering
ML pipelines and MLOps
Model lifecycle done properly — versioned, evaluated, monitored, retrained on a schedule.
Read more - AI / GenAI Engineering
Eval harnesses and continuous evaluation
Domain-specific evals that gate every deploy — no vibes-based shipping, no silent regressions.
Read more - AI / GenAI Engineering
AI product development
End-to-end AI product builds — UX, model, retrieval, eval, and ship. Available on the partnership model.
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Ready to scope llm applications and rag systems?
Book 30 minutes — we’ll tell you honestly whether the partnership model fits or whether an SOW is the better path.