[ ADVISORY_&_CONSULTING ]
Document-Grounded Business Advisor
What it is
An AI advisor that will only tell you things it can point to in your own documents.
[ THE_PROBLEM ]
Why this existed
Generic AI advice is worthless to a business because it knows nothing about that business. The gap is not model quality — it is that the model has never read the company's own documents, and when it guesses, nobody can tell.
[ WHAT_WE_BUILT ]
What we built
Workspaces where a team drops in PDFs, DOCX, CSVs and spreadsheets. A background job extracts text, chunks it, and indexes it into a per-workspace vector namespace. From there two things run: a scored, versioned business audit generated from multi-probe retrieval plus accumulated business memory, and a streaming advisor chat that runs a real agent loop with two tools — search the documents, and save a durable fact about the business. Every answer carries citations. Uploading a new document marks the existing audit outdated rather than silently going stale. Role-based access (owner / admin / member) is enforced on the server for every single function, and every record is scoped to its workspace.
- Ingests PDF, DOCX, TXT, MD, CSV, JSON and HTML into a per-workspace index
- Generates a scored, versioned business audit from multi-probe retrieval
- Marks existing audits outdated when new documents land, instead of going stale silently
- Streams an agent loop with document search and memory-write tools
- Attaches citations to every claim so answers can be checked against the source
- Enforces owner / admin / member roles server-side on every function call
- Scopes every record to a workspace so tenants cannot read across boundaries
[ HOW_IT_IS_USED ]
How a company uses it
A consulting firm, internal strategy team or fractional-CxO practice loads a client's real documents and gets a defensible scored assessment in minutes instead of a fortnight — then keeps the workspace alive as an advisor the client can interrogate, with citations for anything it claims.
Built with
[ COMMON_QUESTIONS ]
Questions clients ask
How is this different from uploading documents to ChatGPT?
Three things: persistence, structure and provenance. The workspace keeps documents, accumulated business facts and audit history across sessions rather than losing them when the chat ends. The audit is a scored structured object generated against a fixed schema, not free prose, so two runs are comparable. And every answer carries citations back to the source chunk, so a claim can be verified rather than trusted.
What stops the AI from inventing things about our business?
Answers are generated from retrieved chunks of your own documents, and the agent has an explicit search tool it must call to get them. Citations are attached to the response so an unsupported claim is visible rather than buried. It does not eliminate error, but it makes error checkable — which is the difference between a tool you can use for advisory work and one you cannot.
Can this run against confidential client documents?
Yes, with the usual caveats. Every entity is scoped by workspace and role checks run server-side, so access control is enforced where it cannot be bypassed by the client. If your requirement is that documents never reach a third-party model provider, that changes the architecture toward a self-hosted or local model, which we have also built — talk to us about the constraint before the design.
[ RELATED_WORK ]
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