Services
Private RAG knowledge assistants
Staff ask questions in plain language and get an answer that points at your own files, not at the open internet.
RAG means the model retrieves passages from a collection you control and answers from those passages. It is the right pattern for policies, manuals, past proposals, and product sheets. It is the wrong pattern if the documents are a mess and nobody will clean them.
Digiting separates the collection, the permissions, and the model. A private deployment is available when a public API is unacceptable. That choice changes the price, because you are then paying for a place to run the model.
An accounting firm searching engagement letters, a clinic searching intake protocols, or a professional firm searching proposals are the shapes we design for. They are examples, not case studies of named clients.
What you receive
- A document set with owners and access rules
- An assistant that cites the passage it used
- A test list of questions and the answers you consider acceptable
- Operating notes for adding a file later
Who it is for
Firms whose knowledge is already written down and whose risk is a staff member answering from memory.
What this can cover
- Document ingestion and permission-aware search
- Cited answers
- Internal or customer-facing assistants
- Local or private hosting options
- A review step when the retrieval is weak
Why businesses use it
- New staff find the current policy
- Answers can be checked against a paragraph
- You are not pasting confidential files into a consumer chat box
Building the first corpus is a project. Adding a modest number of files later can fit the monthly hours. Storage and model hosting are metered separately.
Questions
What is a RAG system?
Retrieval-augmented generation. The assistant searches your documents, reads the relevant passages, and writes an answer that should stay inside those passages. If the search finds nothing useful, it should say so.
How do we implement a private AI assistant?
Choose the documents, decide who may ask, pick where the model runs, and test it on questions you already know the answers to. Digiting scopes that sequence on the contact form before any build.
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