AI search assistant for enterprises and AI assistant for work that knows where every answer lives

An AI search assistant retrieves from your company's content and then writes an answer from what it retrieved, citing each source. It sits between two things people already know: a search engine, which finds documents but leaves you to read them, and a chatbot, which writes fluently but does not know anything about your company. The assistant does the finding and the reading, and shows its work.

The practical test of whether something deserves the name is what it does when the answer is not in your content. A retrieval-grounded assistant has nothing to cite, so the honest output is that nothing was found. A general model asked the same question will produce a confident, plausible, entirely invented answer, because generating text is all it can do. That single behavior is worth testing in every demo, with a question you know your content does not answer.

By what it can see. A general assistant reasons from public training data and whatever you paste into the window, so it is strong at drafting, summarizing and thinking through a problem, and structurally unable to tell you what your own company decided. Tenant-scoped assistants like Microsoft 365 Copilot do reach internal content, but only content that lives inside Microsoft 365, which is a real boundary if half your answers are in Slack, Notion or Jira.

The comparison to make is therefore about coverage and grounding rather than about model quality. Ask which systems the assistant can actually read, whether it enforces the permissions of each of those systems per person, and whether every claim in an answer carries a link to the document behind it. Our Microsoft Copilot alternatives page covers the tenant-boundary question in detail, including what Copilot licensing does and does not include.

It depends entirely on two properties, and both are checkable. The first is whether permissions are enforced at query time or at sync time. Query-time enforcement means that when someone loses access to a document, the very next question they ask cannot draw on it. Sync-time enforcement means your exposure window is however long it takes the next sync to run, which can be hours.

The second is whether your content is used to train models. This should be a contractual statement, not a marketing one, and it should be specific about subprocessors as well as the vendor. InSearch enforces item-level permissions from every connected source at query time and does not train on customer content. That combination is what makes an assistant deployable to an entire company rather than only to a team where everyone already has identical access, which is the quiet reason many pilots never expand.

The failure mode worth naming, because it has happened to real deployments, is an assistant that indexes everything with a single service account and then filters results afterwards. That design puts the whole corpus behind one credential and makes correctness of the filter the only thing standing between a search box and an incident. Ask any vendor to describe their model precisely enough that you could draw it.

The questions it handles best are the ones whose answers exist in writing but are split across systems. What did we tell this customer about the outage. What is the current approval threshold for this spend, and when did it change. Why is this service built this way, and who decided it. How much parental leave applies in this state. Each of those has a written answer somewhere, and each is currently answered by interrupting a colleague who remembers where it lives.

What it cannot do is invent institutional knowledge that was never recorded. If a decision was made verbally and never written down, no assistant will surface it, and any tool that appears to is generating rather than retrieving. The useful side effect of deploying one is that these gaps become visible: the questions that come back empty are a precise, ranked list of what your company knows but has never documented.