Every large company has the same quiet problem: the answer exists, but nobody can find it. It sits in a Slack thread, a SharePoint folder, a Jira ticket, or a slide deck someone built two years ago. Employees waste time hunting for it, or worse, rebuild it from scratch.
The cost is not small. McKinsey Global Institute’s research on interaction workers estimated that roughly a fifth of the workweek goes to searching for and gathering information. That figure is more than a decade old, but anyone who has spent ten minutes looking for a contract or a product spec will recognize it.
Enterprise AI search is the current answer. Instead of returning a list of links, these tools connect to your company’s apps, index the content, respect existing permissions, and return a direct, cited answer. This guide covers ten tools worth considering in 2026, starting with the one that sets the benchmark for AI-native workplace search.
What to Look for in an Enterprise AI Search Tool
Five criteria separate the strong options from the rest:
- Connector coverage. The tool is only as useful as the systems it can read.
- Permission awareness. Results must reflect what each person is already allowed to see.
- Answer quality. Look for natural-language queries and cited answers.
- Deployment effort. Some tools work out of the box; others need engineers.
- Fit with your stack. A Microsoft-first company and a multi-vendor company need different things.
1. Glean
First on the list, Glean remains the strongest all-around choice for companies that run work across many different apps. Its connector library is its biggest advantage: the company cites more than 250 connectors, covering tools such as Slack, Salesforce, GitHub, Dropbox, Gmail, and Microsoft SharePoint and Outlook.
Behind the connectors sits a search index and an enterprise knowledge graph that maps people, content, and interactions, so results are personalized to the person asking. Glean enforces the permissions of each source system, which means employees only see what they can already open. Semantic, vector-based search handles natural-language questions, and Glean Chat and Glean Assistant answer from company data with citations.
Best for: Large companies with a mixed stack that want search, an assistant, and agents in one platform.
2. Microsoft 365 Copilot and Microsoft Search
If your company uses Microsoft 365, the search layer is already in place. Microsoft Search spans SharePoint, Teams, Outlook, and OneDrive, using Microsoft Graph signals to surface documents, people, and messages. Copilot adds cited, conversational answers on top.
The interface is familiar, so training is minimal. The limitation is scope: it works best on Microsoft-owned content, and reaching other systems usually means extra configuration or third-party connectors.
Best for: Microsoft-first organizations of any size.
3. Coveo
Coveo is an AI-powered relevance platform built around personalization. It learns from user behavior to rank results, which makes it a strong fit where search quality directly affects outcomes, such as customer support portals, e-commerce, and employee search across many content sources.
Best for: Large organizations that need advanced, behavior-driven personalization, especially for customer-facing search.
4. Elastic
Elastic, the company behind Elasticsearch, supplies the technology that powers a large share of search products in the market. Built on the Elastic Stack, it offers deep indexing, fine control over relevance, and the scalability to handle very large datasets. It is also open-source and flexible in deployment.
That flexibility comes with a price: you are assembling a solution, not buying one. Expect to dedicate engineering time.
Best for: Teams with strong engineering resources that want full customization or on-premises control.
5. Sinequa
Sinequa focuses on large, complex, and often regulated environments. It offers multilingual, AI-powered search and analytics that suit global enterprises with content spread across languages, formats, and compliance requirements.
Best for: Multinational and regulated organizations with complex content.
6. Guru
Guru blends enterprise search with a verified knowledge base. Its browser extension surfaces answers within the tools people already use, and its verification workflow nudges owners to keep content accurate and up to date. The AI search understands the intent behind questions and highlights the most relevant results.
Guru has plenty of features, so initial setup and onboarding can feel heavy for some teams.
Best for: Teams that want search and a curated, trustworthy knowledge base in one place, such as support, sales, and enablement.
7. Google Cloud Search
For companies on Google Workspace, Google Cloud Search is the natural fit. It brings Google’s search strengths to Gmail, Drive, Docs, and other Workspace content, with little setup. The trade-off is the reverse of Microsoft’s: it shines within the Google ecosystem but requires more effort when your data lives elsewhere.
Best for: Google Workspace teams that do not need broad integrations with non-Google tools.
8. Moveworks
Moveworks combines enterprise search with service workflows. Employees can ask a question and, where appropriate, have the system act on it, which is useful for IT and HR support. Search here is part of a bigger goal: resolving requests, not just finding documents.
Best for: Enterprises that want search tied to employee support and automation.
9. Lucidworks
Lucidworks uses AI and machine learning to deliver relevant search and discovery, with a focus on workforce productivity and commerce-style relevance. It suits large deployments and can handle on-premises requirements.
Best for: Very large or on-premises deployments that need tunable relevance.
10. Slite
Slite is a self-maintaining knowledge base with built-in enterprise search. Its AI agent answers questions from your documentation and connected sources. It is newer than the long-established platforms on this list and lighter-weight, which makes it appealing to teams that want to centralize knowledge without a large rollout.
Best for: Growing teams that want an easy-to-use knowledge base with AI search.
How to Choose the Right Tool
The payoff for choosing well is real: the same McKinsey Global Institute research estimated that better knowledge-sharing tools could raise the productivity of interaction workers by 20 to 25 percent.
Start with your stack. A Microsoft-first or Google-first company should evaluate the native option first, then ask whether it covers enough of the apps people really use. If work is spread across many vendors, a cross-app platform like Glean or Coveo usually gives better coverage.
Next, be honest about your technical capacity. Elastic and Lucidworks reward teams that can build and tune, while Glean, Guru, and Slite are closer to being ready to use.
Finally, run a pilot with real questions. Take twenty queries your employees ask every week and test each shortlisted tool against them. Measure whether the answer is correct, cited, and permission-safe. A demo with clean sample data proves very little.
Search is only one piece of the efficiency puzzle. If you are also reviewing the rest of your tooling, our roundup of software solutions for streamlined operations covers the wider picture.
Final Thoughts
Enterprise AI search has moved from “nice to have” to core infrastructure. The best tools do more than find files: they understand context, protect permissions, and increasingly take action. For most large teams working across many apps, Glean is the strongest place to start. If your needs are narrower, such as a Microsoft-only environment, a developer-led build, or search tied to support workflows, one of the other nine may fit better.
Whichever you shortlist, involve security early and measure adoption, not just features.
