In practice
The same industrial engineering client had engineers spending days pulling pressure ratings, materials, and connection sizes from thousands of inconsistent PDF datasheets. The pipeline returns structured data with cited source snippets so anomalies can be reviewed. Manual extraction that used to take a senior engineer's day is now a background task. That case recorded a 95% reduction in manual extraction time, and more than 10,000 documents processed in days.
Ground AI in your own knowledge
A foundation model on its own is a generalist: confident, articulate, and often wrong about your business. Retrieval-Augmented Generation (RAG) retrieves the right context from your own systems and gives the model the source material it needs to answer accurately, with citations.
We design and build production RAG systems. That means real document pipelines, real evaluation, real cost control, and real maintenance once it's live.
What a production RAG system actually needs
- Robust ingestion: PDFs, Word, Confluence, SharePoint, Slack, Notion, databases.
- Smart chunking and embedding: tuned per content type, not one-size-fits-all.
- Hybrid retrieval: semantic + keyword + metadata filters.
- Reranking so the model sees the right snippets, not just the closest ones.
- Source citations built into the response, so users can verify.
- Evaluation harness: measurable accuracy against a question/answer test set.
- Refresh pipelines: updates to source content propagate to the index automatically.
Where it's most powerful
Customer support knowledge bases, internal policy and procedure assistants, sales enablement, compliance lookups, technical documentation Q&A.
