What is enterprise AI search?
Enterprise AI search lets employees ask a question in plain language and get an answer drawn from the company's own systems, such as SharePoint, Workday, ServiceNow and email. The answer cites the documents it came from, and it only uses documents the person asking is allowed to open.
Why people still cannot find things at work.
Keyword matching
Search for "parental leave" and you miss the document titled "Family and medical leave policy", because the words do not match.
One system at a time
SharePoint search cannot see Workday or the ticketing system, so one answer means checking several apps.
Links instead of answers
People get a list of forty documents when they wanted the one paragraph that answers the question.
Permissions copied by hand
When content is copied into a separate index without its access rules, search can show people files they should never see.
Enterprise AI search in two paragraphs.
Older enterprise search matched keywords and returned a ranked list of documents. Enterprise AI search works out what the question means, finds the passages that answer it across every connected system and writes a short answer with links to its sources. Many products can now take the next step as well, such as opening an IT ticket or starting an expense claim.
The hard part is rarely the language model. It is connecting each system, keeping every document's access rules intact and keeping the index fresh when files change. That is where most of the build effort and most of the cost difference between products sits.
The parts of an enterprise AI search system.
Every product and every custom build has some version of these components. When you compare vendors, ask how each one is handled.
Hybrid retrieval
Vector search finds passages with the same meaning and keyword search finds exact terms like part numbers. Good systems combine both and rerank the results.
A language model for answers
The model reads the top passages and writes a short answer that cites them. It should say so when the sources do not answer the question.
Connectors
Each connector indexes one system, such as SharePoint, Workday, ServiceNow or Salesforce, and keeps the index current as content changes.
Permission trimming
Results are filtered by what the person asking may open in the source system, checked when the question is asked.
Actions
When the question is really a request, the system hands it to a workflow, for example a password reset or a leave request, usually with a confirmation step.
Search analytics
Reports on what people ask, what they cannot find and which sources answer best, so content owners know what to fix.
How enterprise search changed.
| Period | Approach | What it could do |
|---|---|---|
| 2000s | Keyword search | Matched words in file shares and intranets and ranked documents by how often the words appeared. |
| 2010s | Federated search | Searched several systems from one box, still by keyword, with the result lists merged together. |
| 2018 onward | Semantic search | Used vector embeddings to match meaning, so a question about time off finds the PTO policy. |
| 2023 onward | AI answers and actions | Writes a cited answer from the best passages and can start a task when the question is a request. |
A search product or search built in your own cloud.
Both are reasonable. The right choice depends on how many people need it, how sensitive the content is and how many systems it must reach.
| Search product (SaaS) | Built in your Azure or AWS account | |
|---|---|---|
| Where content is indexed | Usually in the vendor's cloud | In your own subscription, for example Azure AI Search or Amazon OpenSearch |
| How you pay | Per user per month, often with a minimum seat count | A build fee, then your own cloud and model usage |
| Time to first answers | Weeks for standard connectors | About two weeks for one scoped set of questions |
| Who controls price changes | The vendor, at renewal | You, through your cloud bill |
| Best fit | Company-wide search across many common SaaS tools | One high-value use case, sensitive content or unusual systems |
Start with one question people ask every day.
Company-wide search projects stall because nobody can say when they are finished. A narrower start works better: pick one group of questions that people ask constantly, such as HR policy questions, IT how-to questions or product specifications for the sales team, and the two or three systems that hold the answers.
Write down twenty real questions and the correct answers before anything is built. Those become the acceptance tests. When the system answers them correctly, with the right citations and without leaking restricted documents, you know it works and you have a baseline for the next group of questions.
What we build when you choose to build.
Permission-aware answers
Every answer is checked against what the person asking may open in SharePoint or the source system.
Citations on every answer
Links to the exact document and passage, so people can check the source themselves.
Connectors for your systems
SharePoint, file shares, Workday, ServiceNow, Salesforce and databases, scoped to the questions that matter.
Acceptance tests
Your real questions and correct answers, signed before the build and rerun whenever content or models change.
Runs in your tenant
Azure AI Search or Amazon OpenSearch in your own subscription, with the code in your repository.
Search analytics
A report of unanswered questions so content owners can fill the gaps.
From workflow to working tool in two weeks.
Scope and sign
We define the workflow, the deliverables, and the acceptance criteria, and sign an agreement on them before anything starts.
Build
We build the tool in your environment, with Claude Code and automated evaluation against your own data.
Pay when it works
$10,000, due only after every criterion in the signed agreement is met. Nothing before that.
A first search build covers one group of questions and the systems that answer them, for example HR policy questions answered from SharePoint and Workday. We sign the acceptance tests with you, build it in your Azure or AWS account in two weeks, and you pay $10,000 only after every test passes. Wider rollouts are quoted once the first one works.
Enterprise AI search pricing compared →
Tell us the workflow →Questions buyers ask.
What is enterprise AI search?
Software that answers employees' questions in plain language using content from the company's own systems, with citations to the source documents. It only uses documents the person asking is allowed to open.
How is enterprise AI search different from enterprise search?
Traditional enterprise search matches keywords and returns a list of documents. Enterprise AI search matches meaning, reads the best passages and writes a short answer that links to them. Many tools can also start a task, such as opening a ticket, when the question is really a request.
How does enterprise AI search keep documents private?
Through permission trimming. The system checks what the person asking may open in the source system and filters out everything else before the answer is written. When you evaluate a product, ask whether permissions are checked when the question is asked or only copied in at indexing time.
Is Microsoft 365 Copilot an enterprise AI search tool?
Partly. Copilot answers from what Microsoft Graph can reach, which is your Microsoft 365 content plus any outside systems you connect through Copilot connectors. It respects Microsoft 365 permissions, so oversharing in SharePoint shows up in its answers. Clean up permissions before you turn it on.
Should we buy enterprise AI search or build it?
Buy when you need company-wide search across many common SaaS tools and the per-user price works for your headcount. Build when you have one high-value use case, sensitive content you want to keep in your own cloud, or systems no product connects to well.
How long does it take to set up?
Products with standard connectors usually take a few weeks to connect and tune. A custom build for one group of questions takes about two weeks once the acceptance tests are agreed. Company-wide rollouts take longer either way, mostly because of permissions and content cleanup.
Ready to ship AI that actually works?
Send us the workflow. We return a fixed scope, price, and acceptance criteria in 48 hours. You pay $10,000 only after it works.
Tell us the workflow →The mid-market AI advantage: How $50M to $500M companies out-ship the Fortune 500
Why $50M to $500M companies get AI into production faster than the Fortune 500.
Read it free →