How to Prepare Company Documents for an Internal AI Assistant
How to Prepare Company Documents for an Internal AI Assistant
The most common source of disappointment with internal AI knowledge systems is not the technology. It is the documents. A knowledge system is only as good as the source material it indexes, and most businesses underestimate what it takes to get that material into a useful state.
This is not a reason to delay. It is a reason to approach the preparation work properly rather than treating it as a formality before the interesting part starts.
Document Types That Work Well
Policies and procedures work well. They are written to be read carefully, they are structured, and they contain specific, accurate information about how the business operates. Standard operating procedures, HR policies, health and safety documents, client onboarding guides, these are ideal candidates.
FAQ documents work well, especially if they have been written from real questions rather than made up in advance. If you have a document where someone has written down the questions staff or clients ask repeatedly and answered them clearly, that document is highly valuable in a knowledge system.
Guides and reference documents work well. Product documentation, pricing guides, supplier terms, service descriptions, anything written to be a reference rather than a narrative.
Contracts and agreements work well for extraction purposes, with the caveat that retrieval should go to the appropriate people rather than being broadly available.
Document Types That Do Not Work Well
Scanned handwriting is the most problematic input. Unless the handwriting is clear and the scan is high resolution, the accuracy of text extraction is too low to be reliable. If your business has important information in handwritten notes, the right approach is to transcribe them before ingestion, not to feed them in directly.
Highly formatted slide decks are poor source documents. The text in a slide presentation is usually fragmented, headline, three bullet points, a label on a chart. Without the accompanying verbal explanation, slides rarely contain enough context to answer a question properly. If the information in the slides is important, write it up as a proper document first.
Fragmented notes, meeting notes where three separate items are listed without context, quick notes in a shared document, Slack messages exported as text, are problematic because the retrieval system cannot easily determine what is authoritative and what is a rough draft or interim decision.
Email threads are a mixed case. They can contain genuinely important decisions and agreements, but they also contain a lot of noise, back-and-forth discussion, corrections, tangents. If an email thread contains a decision that matters, the right approach is to extract that decision into a clean reference document rather than index the whole thread.
What Needs Cleaning Before Ingestion
The most important cleaning task is resolving conflicting versions. If you have a returns policy document and three different versions in different folders, the system will retrieve from all of them and the answers will be inconsistent. Before indexing, decide which version is authoritative and archive or remove the others.
The second cleaning task is handling outdated documents. A supplier agreement from 2019 that no longer reflects your current terms, a pricing guide that predates your last price change, an onboarding checklist that describes a process you changed eighteen months ago, these will produce wrong answers if indexed. Mark them clearly as superseded, remove them from the index, or update them before they go in.
The third cleaning task is adding context that the document assumes the reader already has. A document that says "as per the standard Tier 2 terms" without explaining what Tier 2 terms are will produce incomplete answers. Either update the document to be self-contained or index it alongside the document that defines Tier 2.
How to Structure Documents So AI Can Find Specific Information
Documents with clear headings retrieve better than documents without them. A policy document with a heading for each clause is more useful than a continuous block of prose covering the same content.
Section headings that match the questions people actually ask work best. "What is the policy on client payment terms" is answered more reliably if there is a section heading that says "Client Payment Terms" than if the relevant information is buried in the third paragraph of a section called "Financial Administration."
Where a document covers multiple topics, separate documents per topic tend to work better than one large document. A single document covering HR policies, IT policies, and facility management is harder to retrieve from accurately than three separate documents, one per topic.
The private knowledge system I build for clients includes a document audit as part of the setup, reviewing what exists, identifying conflicts and gaps, and advising on what needs to be cleaned or restructured before the index is built. If you are comparing this to other approaches to internal knowledge management, the private AI system vs SaaS comparison explains what is different about a system built for your specific document set.
The preparation work is unglamorous, but it is what separates a system that works reliably from one that produces inconsistent answers and loses the team's trust within a month. If you want to understand what the process looks like for your document library, request a system review and I can give you a realistic picture of what is involved.