Enterprise AI Search: How to Search Business Documents With AI

Enterprise AI search lets staff ask a question in plain words and get an answer written from the firm’s own documents, with the source shown. Setting it up comes down to six steps and a test. The model is one of those six steps. The rest are decisions the firm makes itself: which documents count, who may see each one and who checks the answers. This guide sets out the six steps in order, what each asks of your own team, how to test the result with real questions and what to ask a supplier or partner. It sits under the Signisys use case on AI knowledge search. Whether to build an assistant on your own files, buy one in a software suite or keep a plain search tool is weighed in a plain comparison of three routes to answers from company documents.

Enterprise AI Search: What You Need Before You Start

Three terms carry through this guide:

• Enterprise AI search: in this guide, a search over the firm’s own files that returns a written answer and names the file it came from.
• Document set: the files that answers may come from.
• Owner: the person who can say which version of a document is current.

Before step 1, gather four things:

• a list of the questions staff ask and struggle to answer;
• the documents that answer them;
• a person who can say who may see what;
• a person who will read the results once staff start using the search.

Check whether each of the four has a name beside it. If one is missing, settle it first. The later steps lean on these four. For example, without a person who decides who may see what, step 3 has no one to make its decision. To check the documents themselves before step 1, see a 15-check sheet for scoring whether company documents are ready for an AI answer tool.

Enterprise AI Search in Six Steps

The six steps below are Signisys’ working method, not a standard. Each one names what the firm’s own team decides, and the sections that follow take them in turn.

The order matters.

Testing comes last, and it reuses the questions from step 1.

Name the Questions and Collect the Documents

Step 1 starts with people, not files. Ask five or six colleagues from different teams for the last few questions they could not answer from the firm’s own documents. Write each question in their words. Beside it, note the document that holds the answer, or write “unknown” if nobody knows. Keep the list; step 6 uses it again.

Then group the questions by the team that asks them. For example, finance may ask about approval limits, while operations asks about the latest site procedure. Each group points to a document set.

Step 2 turns that list into a set of files. For each document, collect one current copy and note its owner and its date. Where two copies differ, the owner decides which one stays. A file with no owner or no date goes on a short list for the relevant team head to decide on.

Where the content of a document sits in scans or forms rather than text, see the use case on extracting data from documents.

Output of steps 1 and 2: a question list, and a document set with an owner and a date against each file.

Enterprise AI Search: Access Rules, Splitting and Indexing

Step 3 decides who may see what. A person asking a question should be shown passages only from documents they may already open. The firm’s access owner makes that decision, not a supplier. Record the rule for each document set: which roles or teams may see it, and who approved the rule. The OWASP RAG Security Cheat Sheet, a security guide from OWASP, says a document’s rules should follow each passage cut from it and be checked when passages are retrieved, and that the model should not be left to enforce them. Where a set mixes open and restricted files, split it into two sets with separate rules. For the wider rules on AI use across the firm, see the use case on governing enterprise AI use.

Step 4 prepares the documents for searching. Splitting means cutting each document into passages, such as the text under one heading. An index is a searchable copy of those passages that points back to the original file. Keep the document’s title, heading and file name with each passage, which lets an answer show where it came from. Treat the size of a passage as a choice to test in step 6, not a number to copy from elsewhere.

For one product’s account of indexing, see our guide to one cloud search service.

Retrieve the Right Passages and Answer With a Source

Step 5 has two parts. First, the retrieval step finds the passages that fit the question. Then a model writes the answer from those passages and shows the file and passage it used. This pattern is called retrieval-augmented generation: the model receives the passages that fit a question and writes its answer from them. The name comes from a 2020 research paper by Patrick Lewis and colleagues, which paired a language model with a searchable index of Wikipedia (arXiv).

The source lets a reader check the answer against the document, and the OWASP cheat sheet likewise recommends returning, with each answer, the documents and passages that were used. If a reader finds no source beside an answer, treat that answer as unconfirmed until someone checks the document. Similarly, when no passage fits a question, a plain statement that nothing was found serves the reader better than an answer written without support.

Before choosing a supplier or partner, ask three questions:

• How does each answer show the file and passage it came from?
• How are the firm’s access rules applied when passages are found?
• What does a person see when no passage fits the question?

Test With Real Questions and Review What People Searched For

Step 6 has a test before launch and a review after it. In Signisys’ working method, the test runs as follows:

1. Take the question list from step 1.
2. Ask each question in the new search, worded as the colleague asked it.
3. Compare each answer with the document that holds the right one.
4. Record what differs: a wrong answer, a missing source or a passage the person should not have seen.

Fix what the record shows, then ask the same questions again. This guide sets no pass mark; instead, the team decides what is good enough for each document set.

After launch, read the searches that returned nothing, as well as any answers staff flagged as unhelpful. Each one points to a missing document, a missing rule or wording that staff use but the documents do not. Add the document or the wording, and then test that question again. For why staff miss answers in the first place, see the five causes of a missed answer in company search.

Name an owner and a deputy who read these results, and write their names beside the question list. A test on past questions shows how the search handles those questions. However, it does not show all the questions staff will ask later.

What Enterprise AI Search Leaves to Your Own Team

The six steps give an order of work, not the answers to the firm’s own questions. Which documents belong in each set, who in the firm may see what, and what a given product or build involves all stay with your team. This guide is a method, not an audit or legal advice, and it promises no particular result.

Bring in an outside view when two teams each keep a different “current” version of the same document and nobody can settle which one counts.

Signisys provides data and AI services through its enterprise AI solution and its data and AI service page. This box describes a Signisys service, not neutral advice, and it makes no promise.

Enterprise AI Search FAQ