Document search & AI assistants

Turn a collection of files into usable answers

A useful document assistant needs to do more than return a confident paragraph. It must find the right version of a document, respect the reader’s access, cite the relevant passage, and recognize when the source material does not answer the question.

I build search and assistant workflows around those requirements. Depending on the problem, that may mean better conventional search, retrieval-augmented generation (RAG), or a combination.

Where this can help

Illustrative applications include internal policy lookup, engineering manual search, client-file summaries, property document retrieval, and support knowledge assistance. The first scope should cover one document collection and a defined group of readers.

Documents may remain in your approved cloud services or be processed inside a private environment. If their contents cannot go to an external model provider, the retrieval and generation components can be designed around a private AI deployment.

What we build

The document pipeline extracts text and retains source information such as document title, version, page, and access permissions. Search retrieves passages that the current user is allowed to see. The assistant generates an answer from that material and returns links or references for review.

The application also needs a way to handle changed files, withdrawn documents, stale indexes, and permission changes. These lifecycle details are part of the system design.

A pilot includes a question set with expected sources. We test straightforward questions, ambiguous terminology, missing answers, conflicting versions, and attempts to retrieve restricted material. The evaluation separates search failures from answer-generation failures.

Deliverables and acceptance

A typical scope includes the ingestion workflow, search or assistant interface, identity integration, source references, evaluation results, and operating documentation. The proposal names the supported file types and systems, the expected scale, and the person responsible for approving results.

Acceptance criteria can include finding the correct source for a defined question set, showing the relevant citation, refusing unsupported answers, and preserving access boundaries. Targets are agreed with your team rather than inferred from a generic model benchmark.

What to bring to a conversation

A description of the document collection, where it lives, who may access it, and several real questions your team struggles to answer. We can start with synthetic or appropriately sanitized samples.

No model can compensate for a missing policy or unresolved conflict between two authoritative documents. Identifying those gaps is often part of the assessment. Read the private RAG guide for the engineering decisions behind the workflow.