Find Anything Across Your Systems with AI Enterprise Search

Keyword search matches literal words and misses documents described differently, and it cannot answer a question, only return links. Enterprise search understands meaning, retrieves across systems, and gives a cited answer. The difference is that it finds what you meant and shows its source, rather than a list you still have to read.

Yes, and this is essential. Every result is filtered to what the individual user is allowed to see, so search never surfaces restricted content. Permission-aware retrieval is a hard requirement, not a setting, because a search tool that leaks documents across access boundaries is a serious risk.

Not if it is grounded, which is how we build it. Every answer is grounded in retrieved passages with citations, and the system says when it cannot find a relevant source rather than inventing one. An answer with no source is unusable in an enterprise, so we design against it directly.

Common ones are SharePoint, Confluence, Google Drive, wikis, and internal document stores, connected through their APIs with permissions preserved. We start with the sources holding the most-needed knowledge rather than connecting everything at once, so value comes early and the index stays manageable.

You do. Your documents, the index, and the code remain yours, in your environment, with permissions intact. We build on your stack and hand over documentation, so there is no lock-in to us in what we deliver.

There is no single price; cost tracks scope. A focused build around permission-aware document search is a modest, weeks-long project, while a wider rollout across your document and knowledge systems is larger. We scope from one use case, quote a fixed range up front, and sequence so early value funds the next step rather than pricing everything at once.

The return comes from less time searching and less duplicated work, set against build and running cost. It only holds when the model targets a real, measured cost, so we baseline first and report value against it. We would rather size the return honestly on your numbers than quote an industry average that may not fit you.

A focused pilot usually reaches a working version in a few weeks, then tuning on real data, with wider rollout taking longer. We start narrow, prove the numbers, and extend, so you see value early instead of waiting months for one large launch.

Common ones are document question answering, semantic search, cited retrieval, and cross-system knowledge lookup. The best first project is the one tied to your biggest measurable cost or opportunity, not the most advanced-sounding option. We help you pick the use case where value is fast and the data already supports it.

We start with a short feasibility check on one use case: does the data exist, is it usable, and does it hold the signal the model needs. Often you already have more usable data than you expect. We assess it before recommending any build, so the first step is a decision, not a commitment.