RAG systems
Retrieval-augmented generation over your private documents, databases and APIs.
Retrieval-augmented generation over your private documents, databases and APIs.
Multi-step agents that plan, call tools and complete real workflows.
Copilots, assistants and search experiences embedded in your product.
Domain adaptation and fine-tuning where it measurably beats prompting.
Automated eval pipelines, safety filters and human-in-the-loop review.
Model routing, caching and optimization to keep quality high and cost low.
We pressure-test the goal, map the data and define what success measures.
Architecture and approach that's feasible and durable — not just demo-ready.
Senior pods ship working software every week, with you in the loop.
We harden, monitor and optimize for production, then grow with you.
RAG and evaluation keep answers grounded in your real data.
Guardrails, audit trails and access control built in from the start.
Latency, cost and reliability engineered for real usage, not demos.
What is retrieval-augmented generation (RAG)?
RAG grounds a language model in your own data by retrieving relevant context at query time — dramatically improving accuracy and reducing hallucination.
Can you build autonomous AI agents?
Yes. We build agents that plan, call tools and APIs, and complete multi-step tasks, with the guardrails and evaluation needed to run them safely.
Do you fine-tune models or use prompting?
Both — we use whichever measurably performs best for your use case, and often combine RAG, prompting and fine-tuning.
Tell us what you have in mind — we'll map a path from concept to production and reply within 24 hours.