RAG & LLM systems
RAG assistants & document intelligence
Language AI over your manuals, standards, and reports — with answers that cite their sources.
First working version in 4–8 weeks
The problem
Your organization's knowledge lives in thousands of PDFs, wikis, drawings, and reports. People spend hours finding answers that exist, and generic chatbots are not an option when a wrong answer has consequences.
The difference between a demo chatbot and a system your team relies on is retrieval quality, grounding, evaluation, and knowing when the system should say "I don't know".
Our approach
Corpus and question audit
We analyze what documents you have, what questions matter, and what a correct answer looks like — before choosing any technology.
Retrieval design
Chunking, indexing, and hybrid search tuned to your document types: tables, drawings, standards, multilingual content.
Grounded generation with citations
Every answer linked to its source passages, with confidence handling and explicit refusal when the corpus has no answer.
Evaluation before rollout
A test set of real questions from your team, measured for accuracy and groundedness — the gate for going live.
Integration and guardrails
Delivered where people work (web, Slack/Teams, API), with access control, logging, and human-review loops.
Reference architecture
- Document ingestion pipeline (PDF, Office, wikis, databases)
- Vector and hybrid search index with metadata filtering
- LLM orchestration with grounding and citation enforcement
- Evaluation harness with your team's real questions
- Access control, usage logging, and feedback capture
- Web, chat-platform, or API delivery
What you get
- An assistant that answers from your documents, with citations
- A measured accuracy report on real questions from your team
- Admin tooling to add, update, and retire documents
- Clear cost and latency characteristics before you commit to scale
Proof. We build language-AI products ourselves — Lemma, an AI review panel for scientific writing, and DropPlot, automated data analysis — so we know what it takes to ship LLM systems, not just demo them.
Ask your documents a hard question.
A 30-minute call is enough to tell you whether this is feasible on your data.