Enterprise search once meant typing a few keywords into a SharePoint box and hoping the right document happened to contain them. That era is largely over.

The platforms profiled below no longer return a simple list of links. They read across dozens of connected systems, respect the permissions that already exist in each one, and generate an actual answer with a citation attached. Under the hood, most rely on retrieval-augmented generation (RAG): instead of letting a language model answer from its training data alone, the platform first retrieves relevant passages from the company's own documents and grounds the generated response in them — the reason citations, and the reason answers can be checked against a source.

The shift has been driven by a real pain point: knowledge workers routinely spend a meaningful share of their week hunting for information scattered across drives, chats, wikis, and SaaS apps, and traditional keyword search never solved that sprawl. Generative AI turned search from a lookup tool into an answer service, and every major vendor — plus a crop of well-funded startups — has moved into the category.

Some are horizontal "search everything" platforms; others are narrower, built for a specific ecosystem or for buyers who want full control over their own infrastructure. Here are ten that are genuinely running inside companies right now.

Glean

Glean is probably the platform most people picture when they hear "AI enterprise search" in 2026: a permissions-aware knowledge graph spanning more than a hundred connected apps, with search, an AI assistant, and agent-building tools all sitting on the same underlying index.

It has grown fast enough to reach a $7.2 billion valuation, and its pitch leans hard on model neutrality: customers can route different kinds of queries to different LLMs depending on internal policy, rather than being locked into one vendor's model.

The limitation is that it is cloud-only and its natural-language understanding is optimized primarily for English — a constraint that matters less for a US tech company and more for a global enterprise running significant amounts of non-English content.

Microsoft Copilot (Microsoft 365)

Copilot's search advantage is not really about a smarter algorithm. It comes from Microsoft Graph, which already understands the relationships between people, documents, meetings, and conversations across Outlook, Teams, SharePoint, and OneDrive.

Ask it a question and it can pull context from a recent meeting transcript, cross-reference a related document, and draft a response, all without leaving the Microsoft ecosystem.

That is the strength and the limitation in one package. For an organization that is genuinely Microsoft-centric, Copilot is close to a free upgrade layered on top of tools already paid for. For anyone running a mixed stack with a lot of content living outside 365, Copilot's view of the company stops at the ecosystem's edge.

Coveo

Coveo occupies an interesting middle ground. It is one of the few platforms on this list built to serve customer-facing search (support articles, product recommendations) and internal employee search from the same underlying engine, rather than treating them as two separate products.

It unifies content from more than fifty sources into one index and layers retrieval-augmented generation on top to produce grounded, cited answers — which, for support organizations, translates directly into case deflection: fewer tickets escalated to a human because the AI answer was good enough.

Its personalization layer, tuned by clickstream and behavioral signals, is a genuinely different capability from what most of the purely internal-knowledge tools here offer, and it is part of why Coveo shows up so often in retail and B2B commerce deployments rather than just IT-department knowledge bases.

Elastic

Elastic's search engine has been the open, developer-facing backbone under a huge number of company-built search experiences for years — long before "AI enterprise search" was a category anyone marketed around. It is also the engine on which many of today's AI search stacks were originally prototyped.

What has changed is that Elastic has layered real RAG capabilities and AI features on top of that core (vector search, hybrid retrieval, integration with whatever LLM a team wants to plug in) rather than replacing the underlying engine wholesale.

It is a strong fit for technical teams that want to build a custom search experience with real control over the architecture, and a much less obvious choice for a business team that just wants something to work out of the box without engineering involvement.

Sinequa (by ChapsVision)

Sinequa has built its reputation around one specific, unglamorous problem: making generative AI search work reliably across the messiest kind of enterprise data — heterogeneous systems, dozens of languages, and decades of accumulated content across a global organization with regulatory obligations layered on top.

It has been recognized repeatedly by Gartner for exactly that kind of complex deployment, which is a different achievement from winning over a fast-growing cloud-native startup.

Sinequa is not the platform a ten-person team reaches for on a Tuesday afternoon; it is the one a pharmaceutical company or a global bank calls in when nothing simpler has survived contact with their actual data.

Algolia

Algolia built its name as search-as-a-service for developers: the tool product teams reach for when they need fast, relevant search embedded directly into a website or app, not necessarily an internal knowledge tool for employees.

Its more recent expansion into NeuralSearch and generative answering brought real semantic understanding and AI-generated responses into a platform that used to be almost entirely about speed and relevance tuning through an API.

Algolia is less about connecting to a company's internal Slack and Confluence sprawl and more about powering the search box a customer actually types into on a product's own site — a meaningfully different job from what most of the other platforms on this list are doing.

Azure AI Search

Azure AI Search is infrastructure more than a finished product: a scalable engine that indexes structured and unstructured content and exposes it through APIs for applications, agents, and internal tools to query, rather than shipping a polished, ready-to-use search interface out of the box.

It supports both traditional keyword workloads and modern RAG pipelines, plus agentic search capable of interpreting intent and chaining together multiple steps rather than returning a single flat answer. That agentic capability reflects a broader direction across the category: search is increasingly becoming a building block that AI agents call, not just a tool humans query directly.

The natural buyer is a team already building on Azure that wants a search layer it can wire directly into its own applications — not a business user expecting to log into a dashboard on day one.

Google Vertex AI Search

Vertex AI Search is Google's answer to the same problem. It handles semantic retrieval across both structured data and messy unstructured content such as PDFs, generating grounded answers with visibility back into the source material rather than a black-box summary.

It ties in naturally with the rest of Google's Workspace and cloud ecosystem, which is exactly why it tends to appear on shortlists for organizations already committed to Google over Microsoft. That is also why it offers comparatively limited value to a company running Microsoft 365 or Box as its primary document environment.

Governance and customization have been a real focus area, letting organizations control how deployment and access actually work rather than accepting a one-size-fits-all default.

Guru

Guru takes a lighter-weight approach than most of the platforms above. Instead of trying to be the single, all-encompassing search layer for an entire enterprise, it positions itself as an AI knowledge layer that lives inside tools people already have open all day — particularly Slack, Microsoft Teams, and the browser itself.

It connects Slack, Teams, Google Workspace, Salesforce, and a handful of other systems into one governed, permission-aware knowledge base, but the emphasis is squarely on quick, contextual answers surfacing where work already happens, rather than a separate destination employees have to remember to visit.

That makes it a genuinely different pitch from Glean or Sinequa: smaller in scope, but often faster to get adopted by a team that does not want to learn a new interface.

Onyx (formerly Danswer)

Onyx is the open-source option on this list, and that distinction matters more than it might sound. It gives organizations a natural-language interface to LLMs connected to their own internal documents and applications, with the option to self-host the whole thing rather than sending data to a vendor's cloud.

Teams can chat with connected models, search across organizational data, build custom agents, and automate workflows, all inside infrastructure they control end to end.

It is a natural fit for engineering-heavy organizations, particularly in regulated industries where sending sensitive data outside the company's own walls is not really an option — even though that control comes with more setup and maintenance responsibility than a fully managed SaaS platform would ask for.

Being open-source also means the roadmap is not dictated entirely by one vendor's product decisions. A team can extend or modify the platform directly if something is missing, which is not an option with any of the closed platforms higher up this list.

Taken together, the list maps a market that is consolidating at the top around big ecosystems — Microsoft, Google, AWS — while independent platforms compete on breadth, multilingual depth, personalization, or control. For buyers, the practical question in 2026 is less "which search engine is best" and more "where does our content actually live, and who is allowed to see it" — because that, more than any feature comparison, determines which of these ten platforms fits.

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