The knowledge problem
A ten-year-old business has thousands of documents, tens of thousands of emails and a support history nobody has read end to end. All of it is technically searchable, in the sense that keyword search exists. In practice nobody finds anything unless they already know it’s there.
Retrieval-augmented generation fixes the shape of that problem. You ask a question in ordinary language, the system finds the passages that actually bear on it, and an assistant answers using only those passages — with links back to the source so you can verify.
What we build
Ingestion. Connectors into the places your knowledge actually lives — SharePoint, Drive, Notion, Confluence, your ticketing system, your file server, your CRM. PDFs, spreadsheets, scans, email threads, all of it normalised.
Retrieval. Chunking strategy tuned to your document types, hybrid keyword-plus-semantic search, and re-ranking. This is where most implementations quietly fail — retrieval quality sets the ceiling on answer quality, and no amount of prompt engineering raises it afterwards.
Permissions. People see only what they were already allowed to see. Access rules are enforced at retrieval, not filtered out afterwards.
Evaluation. A benchmark set of real questions with known-good answers so we can measure whether a change made things better or just different.
Governance built in
We keep a register of what’s ingested, where it came from and who can reach it. Sensitive categories can be excluded at ingestion. Everything is logged. If you operate under GDPR, ISO 27001, SOC 2 or FCA obligations, tell us at the start and we’ll design to it rather than retrofitting.
The compounding asset
A working retrieval layer is the thing most businesses should build first. Every agent, copilot and automation you build afterwards gets to stand on it — which makes each of those projects faster and better than it would otherwise be.