AI Document Processing and Company Knowledge Search

Two problems, two connected workflows

Company knowledge search with RAG

Retrieval-augmented generation retrieves relevant approved material to support an AI-generated answer. It helps staff navigate SOPs, policies and manuals while retaining source links. It requires maintained documents, access controls and a response to missing or conflicting evidence.

AI document processing

Processing extracts or classifies information, such as document type or agreed intake fields. The result passes validation and, where needed, human review before entering another system. OCR, extraction, search and automation are separate capabilities to assess.

Practical workflows to explore

SOP and policy questions

Retrieve relevant approved instructions with references, and flag gaps or conflicting versions. Document owners remain responsible for the underlying directions.

Operational document intake

Classify recurring documents, extract agreed fields and flag missing or inconsistent information for review before approved output moves onward.

Contract and reference research

Help an authorised employee locate and summarise clauses in permitted files. Staff check the original before legal or contractual decisions.

Internal service support

Help staff find approved HR or operational guidance while preventing restricted content from appearing in answers or citations. Route individual or interpretive questions to the responsible team.

What makes a good starting collection?

Begin with a bounded collection, a clear owner, recurring questions and reliable source material. Include difficult files as well as clean examples.

1. Representative documents you are authorised to use.
2. An agreed source of truth and update process.
3. User groups and access rules.
4. Example questions or fields defining the task.
5. A destination for reviewed output when integration is needed.
6. A business owner who can judge usefulness.

Poor scans, handwriting, complex tables and inconsistent layouts need testing. Resolve unclear permissions and duplicate policies before broad access.

What we scope and deliver

A knowledge engagement can include collection assessment, ingestion, retrieval design, answer interface, citations, access controls and an evaluation set. Processing can add extraction schemas, validation, exception handling and agreed export or system connections.

The statement of work identifies formats, volume, refresh methods, integrations and responsibilities. Knowledge search does not automatically include custom extraction or access changes across every repository.

Keep the source and answer connected

The security boundary includes temporary files, embeddings, logs and backups as well as source documents. Apply permissions during retrieval and display. Define how removed or replaced documents are updated. When evidence is insufficient, the answer should say so rather than inventing policy.

For extraction, decide what needs review, what can be validated automatically and how downstream rejections are handled. Model confidence alone does not prove a field is correct. See private deployment options.

Build around measurable quality

1. Select the collection, task and current effort.
2. Establish source ownership, versions, formats and access.
3. Build only the scoped retrieval or extraction workflow.
4. Evaluate answer support, extraction accuracy and permission boundaries with representative cases.
5. Review failures and update sources before expanding.

Track time saved alongside correct answers, supported citations, errors, review effort and successful exception handling.

Start the conversation

Describe where your files live, who uses them and what staff need to accomplish. Start with non-sensitive examples; agree a suitable process before assessing protected material.