Natural Language Processing

Retrieval-Augmented Generation (RAG)

Grounded, citeable answers over your own documents, with evaluation harnesses that keep the system honest in production.

Answers your team can trust

We build retrieval pipelines that ground LLM answers in your content, with citations, guardrails, and offline evaluation so quality does not drift after launch.

Hybrid semantic + keyword retrieval
Citation and grounding guardrails
Evaluation harnesses and regression tests
Azure OpenAI and frontier-model integration

24/7

Production RAG assistants shipped

Scope a RAG assistant

Interested in retrieval-augmented generation (rag)?

Back to Natural Language Processing Start a conversation