How to Prepare Your Company Data for AI Agents and RAG

Retrieval-augmented generation (RAG) lets an AI assistant answer questions using your own documents, tickets and records instead of only what the model learned in training. AI agents go further and act on that information. In both cases, the quality of the answers depends heavily on the quality of the data you feed in.

If your shared drives contain five versions of the same policy, the assistant may quote the wrong one. If permissions are loose, it may surface information people should never see. A few weeks of data preparation usually does more for accuracy than switching to a bigger model.

Step 1: Start from the use case

Pick one clear job, such as answering HR policy questions, helping support agents resolve tickets or summarising contracts. Then list the questions people actually ask and the sources that hold the answers. This keeps the scope small and makes it easy to measure whether the assistant is helping.

Step 2: Inventory and choose sources

• Find the sources of truth

  Identify where the authoritative version of each type of information lives, such as the HR system, the help centre or the contract repository.

• Leave out the noise

  Exclude drafts, personal folders, old project archives and chat history unless they are genuinely needed.

• Record ownership

  Give every source an owner who is responsible for keeping it accurate.

Step 3: Clean and structure the content

• Remove duplicates and outdated versions

  Keep one current version of each document and archive the rest outside the AI system.

• Add useful metadata

  Titles, dates, owners, departments, regions and document types help retrieval find the right passage and let the assistant cite it.

• Fix formatting problems

  Scanned PDFs need text recognition, tables need to stay readable, and very long documents work better with clear headings.

• Write down the unwritten

  If answers only live in experts' heads, capture them as short FAQ articles first.

Step 4: Protect sensitive data

Treat an AI assistant like any other system that can read company data. Security and privacy decisions need to be made before launch, not after an awkward answer.

• Respect existing permissions

  Filter retrieved content by the user's access rights so people cannot see answers from documents they are not allowed to open.

• Classify and minimise

  Exclude or mask personal, financial and health data that the use case does not need.

• Check vendor terms

  Confirm where data is stored and processed, how long it is kept and whether it is used to train models.

• Log and review

  Keep records of questions, retrieved sources and answers so that issues can be investigated.

Step 5: Keep it fresh

Data preparation is not a one-off project. Set up automatic syncing from each source, schedule reviews for important content and give users a simple way to flag a wrong or outdated answer. Each flagged answer usually points to a document that needs fixing, which improves the assistant for everyone.

How to know the data is ready

• A test set of 50 to 100 real questions, each with the correct answer and source, agreed with the business owner.

• The assistant answers most test questions correctly and cites the right documents.

• Permission tests confirm that restricted content never appears for users without access.

• Every connected source has an owner and a review schedule.

Where NPLUS Global helps

We help you choose the first use case, audit and clean the source content, design permission-aware retrieval and build an evaluation set so you can prove the assistant works before rolling it out. Our cybersecurity team reviews data protection and vendor settings alongside the build, so speed and safety go together.