10 Enterprise Search Solutions Built With Generative AI In 2026
Enterprise search used to mean typing a couple of words into a SharePoint box and hoping the right document happened to contain them. That era is basically over.
The platforms below don’t return a list of links anymore. They read across dozens of connected systems, respect whatever permissions already exist in each one, and generate an actual answer with a citation attached.
Some are horizontal “search everything” platforms; others are narrower, built for a specific ecosystem or a specific kind of buyer who wants full control over their own infrastructure. Here are ten 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’s 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’s cloud-only and its natural-language understanding is optimized primarily for English, which matters less for a US tech company and more for a global enterprise running significant non-English content.
Microsoft Copilot (Microsoft 365)
Copilot’s search advantage isn’t really about a smarter algorithm.
It’s about 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’s exactly the strength and the limitation in one package: for an organization that’s genuinely Microsoft-centric, this 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’s 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 those 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 actually good enough.
Its personalization layer, tuned by clickstream and behavioral signals, is a genuinely different capability than most of the purely internal-knowledge tools here offer, and it’s 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.
What’s 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’s 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 whole reputation around one specific, unglamorous problem.
It’s making generative AI search actually work reliably across the messiest kind of enterprise data: heterogeneous systems, dozens of languages, decades of accumulated content across a global organization with regulatory obligations layered on top.
It’s been recognized repeatedly by Gartner for exactly that kind of complex deployment, which is a different achievement than winning over a fast-growing cloud-native startup.
Sinequa isn’t the platform a ten-person team reaches for on a Tuesday afternoon; it’s 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.
It’s 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, which is a meaningfully different job than most of the other platforms on this list are doing.
Azure AI Search
Azure AI Search is infrastructure more than it’s 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.
The natural buyer here is a team already building on Azure that wants a search layer they can wire directly into their 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, handling semantic retrieval across both structured data and messy unstructured content like PDFs, and 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 show up on shortlists for organizations already committed to Google over Microsoft.
That’s also why it offers comparatively limited value to a company running Microsoft 365 or Box as its primary document environment instead.
Governance and customization features 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 actually get adopted by a team that doesn’t 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 like it should: it gives organizations a natural-language interface to LLMs connected to their own internal documents and applications, but 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’s a natural fit for engineering-heavy organizations, particularly ones in regulated industries where sending sensitive data outside the company’s own walls isn’t really an option, even if 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 isn’t dictated entirely by one vendor’s product decisions. A team can extend or modify the thing directly if something’s missing, which isn’t really an option with any of the closed platforms higher up this list.
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
