I've been tracking this space closely because the infrastructure story matters as much as the LLM choice. The connector library, the permission-sync pipeline, the admin controls for auditing what was retrieved and by whom - these are what separate enterprise-grade tools from demos that look good until an engineer asks why finance data showed up in an all-hands Q&A.

Methodology

I evaluated these tools across four axes that matter operationally:

1. Connector count and quality - raw number of integrations is less important than whether they support incremental sync and two-way permission mirroring
2. Permission-aware retrieval - does the system respect source ACLs at query time, or does it index everything and filter later (or worse, not filter at all)?
3. Citation and provenance - does the answer surface which document it came from, with a link and timestamp?
4. Admin controls - audit logs, per-connector enable/disable, PII detection, and rate limiting

A tool with 200 connectors but no permission sync is not enterprise-ready. I weight the security architecture more heavily than the feature surface.

Ranked Comparison Table

1. Glean

Glean is the category leader and the tool other vendors benchmark against when they write their comparison pages. The core product indexes your entire enterprise software stack - Slack, Gmail, Drive, Confluence, Jira, Salesforce, GitHub, ServiceNow, and 100+ others - and exposes a unified semantic search interface where employees ask questions and get answers with source citations.

What it does well: The permission model is the standout feature. Glean syncs ACLs from every connected source in real time. If someone loses access to a Confluence space at 2pm, they cannot retrieve content from that space via Glean at 2:01pm. That is not the behavior you get from tools that do a nightly permission snapshot or that trust the LLM to figure out what to surface. The answer quality is high because it combines dense retrieval (vector search), keyword recall, and knowledge graph traversal - so it finds the Slack message where the decision was made, the Confluence doc that documented it, and the Jira ticket that implemented it, then synthesizes across all three.

Glean Agents extends this into workflow automation: agents can answer multi-step questions that require assembling context from multiple sources, escalating to humans, or triggering downstream actions.

Pricing: Enterprise-only, custom pricing. Expect six-figure annual contracts for organizations of meaningful size. No self-hosted option.

Best fit: Mid-market to large enterprises that want a turnkey solution and have the budget for it. The ROI argument is real if your engineers spend non-trivial time searching for internal knowledge.

Honest gotcha: The pricing is opaque and the sales cycle is long. Small organizations and startups rarely find the cost justified relative to simpler alternatives. And while Glean's connector library is deep, actually configuring OAuth permissions for 30 different systems is a multi-week implementation project.

More: glean.com

2. Guru AI

Guru started as a structured knowledge base - the tool your customer support team uses to store verified answers - and has built an AI layer on top of it. The combination works better than you might expect. Verified knowledge cards give the LLM a curated signal layer above raw document retrieval, which reduces hallucination on high-stakes queries (like pricing, policy, or support escalation paths) where inaccurate answers have real consequences.

What it does well: The knowledge verification workflow is Guru's real differentiator. An expert marks an answer as verified. That answer gets surfaced preferentially. Stale answers trigger review reminders. This content governance layer is something pure retrieval tools don't have - they'll happily surface a Confluence page from 2019 that contradicts current policy. Guru's AI uses both the structured card layer and broader indexed content (Slack, Drive, etc.) to answer questions, but it signals which sources are verified versus raw retrieval.

Pricing: Published pricing at www.getguru.com/pricing. The All-in-One plan starts at $18/user/month with a free tier available for small teams. Enterprise pricing is custom.

Best fit: Customer-facing teams (support, sales, onboarding) where answer accuracy is critical and wrong answers have measurable downstream cost. Also strong for organizations that want to gradually build a curated knowledge base rather than index everything raw.

Honest gotcha: Guru is at its best when someone invests time in maintaining the knowledge base. If the organization doesn't have a content governance process, the AI layer is just retrieval on top of whatever is in Drive and Slack - no different from other tools but more expensive.

More: www.getguru.com

3. Microsoft Copilot for M365

If your organization runs on Microsoft 365, Copilot for M365 is the path of least resistance. It indexes SharePoint, Exchange, Teams, OneDrive, and the rest of the M365 ecosystem, and it uses Microsoft Graph to respect the existing permission model you've already configured in Entra ID (formerly Azure AD). That permission model is Microsoft's strongest enterprise argument - most large organizations have already done the hard work of getting their IAM right in Entra ID, and Copilot just inherits it.

What it does well: Native integration is deep in a way that third-party tools can't fully replicate. Copilot in Teams can summarize a meeting, answer questions about decisions made in the meeting, and pull in related SharePoint context - all in one step. Copilot in Outlook drafts email based on prior thread context and relevant internal documents. The LLM (GPT-4 class) is solid, and the tight loop between productivity apps and the knowledge layer reduces the friction of actually using AI in daily work.

Pricing: $30/user/month add-on on top of existing M365 Business Premium or E3/E5 subscriptions. Full pricing at copilot.microsoft.com.

Best fit: Enterprises heavily invested in the Microsoft stack. If SharePoint is your intranet, Teams is your communication layer, and Entra ID manages your identity, the incremental cost is probably justified.

Honest gotcha: Outside the Microsoft ecosystem, Copilot M365 is a much weaker product. It does not search your Notion, your Jira, or your Salesforce with anything like the depth that Glean does. It is a Microsoft-stack search tool that happens to also cover some third-party data, not a universal knowledge layer.

More: copilot.microsoft.com

4. Atlassian Rovo

Rovo is Atlassian's answer to the enterprise AI search category, built on top of their existing Intelligence features and expanded significantly in 2024 and 2025. The core of the product is tight integration with Confluence and Jira - which already makes it the right choice for organizations where those two tools are the canonical knowledge and project management system.

What it does well: Rovo Search operates across both Atlassian products and connected third-party tools (Google Drive, GitHub, Slack, Box, and others) with a focus on surfacing organizational knowledge in context. Rovo Agents - autonomous AI teammates - can take action inside Jira and Confluence, not just retrieve from them. Creating documentation, updating tickets, summarizing project status across open issues: these are real workflow integrations rather than read-only search.

Pricing: Rovo is available as part of Atlassian's Cloud plans. Base access starts from approximately $7.50/user/month within the Atlassian Cloud Premium ecosystem, with full feature access on Enterprise plans. Check www.atlassian.com/rovo for current pricing.

Best fit: Engineering and product teams whose knowledge lives primarily in Confluence and Jira. The integration depth there is better than any competitor.

Honest gotcha: If your organization's knowledge is spread evenly across Notion, Confluence, Slack, and Drive, Rovo's Atlassian-first orientation becomes a limitation. The third-party connectors are functional but not the same depth as the native Atlassian integration.

More: www.atlassian.com/rovo

5. Moveworks

Moveworks takes a different approach from pure search tools - it is an AI assistant platform that sits on top of enterprise systems and handles employee service requests (IT help desk, HR queries, facilities) as much as knowledge retrieval. The knowledge search capability is embedded within a broader agentic workflow that can take action in downstream systems.

What it does well: The integration library is legitimately deep at 500+ enterprise systems, making it one of the broadest in the category. The focus on action (creating a Jira ticket, resetting a password, filing an HR request) rather than just retrieval is where Moveworks differentiates. For large enterprises with high-volume employee service workloads, the ability to resolve Tier 1 IT and HR tickets autonomously is the business case - knowledge search is one component of that.

Pricing: Enterprise-only, custom contracts. Moveworks targets organizations with thousands of employees and the pricing reflects that.

Best fit: Large enterprises where IT and HR service costs are a significant budget line and where automating ticket resolution is a measurable goal. Less appropriate as a pure knowledge search tool for smaller organizations.

Honest gotcha: The platform complexity is real. Moveworks implementations take months and require significant IT involvement. If all you want is a smarter Confluence search, this is substantially more than you need.

More: www.moveworks.com

6. Onyx - The Open-Source Option

Onyx (formerly Danswer) is the most serious open-source contender in this category. It is self-hostable under an MIT license, supports 40+ connectors (Confluence, Slack, GitHub, Jira, Google Drive, Notion, and more), and critically, it does permission-aware retrieval by mirroring ACLs from each source at sync time. That is the right architecture - it is not indexing everything and hoping the LLM doesn't surface restricted content.

What it does well: The open-source model means you control your data pipeline entirely. No SaaS vendor touches your internal knowledge. For regulated industries (finance, healthcare, defense) or organizations with strict data residency requirements, this is not just a preference - it is a compliance necessity. The permission sync model is architecturally sound: Onyx queries source APIs for permission state and updates its index accordingly on sync cycles. The LLM layer is configurable - you can run it against OpenAI, Anthropic, or a local model depending on your data sensitivity.

Pricing: Open-source self-hosted is free. Onyx Cloud starts around $10/user/month. Enterprise licensing for additional support and features is available. Check onyx.app for current plans.

Best fit: Technical organizations with engineering bandwidth to manage the deployment, or any organization with data residency requirements that prevent sending internal knowledge to a SaaS vendor's servers.

Honest gotcha: Self-hosting means you own the operational overhead. Keeping sync pipelines healthy across 20 connected sources, managing embedding model updates, and debugging permission sync failures requires ongoing engineering attention. The managed cloud option reduces that burden but reintroduces the SaaS data exposure question.

More: onyx.app

7. Notion AI with Enterprise Search

Notion AI has matured significantly from its early "AI writing assistant" framing. The current enterprise product includes Q&A capabilities that search across your Notion workspace, generate summaries, and answer questions grounded in your organization's documented knowledge. The Q&A feature can work across all pages a user has access to, which means permission handling is workspace-role-based.

What it does well: Notion users get AI embedded directly in the product they already use for documentation. There is no context-switching - ask a question in a Notion page and get an answer referencing other Notion content. For organizations where Notion is the primary knowledge system, this is genuinely useful without requiring any implementation work.

Pricing: Notion AI is available as an add-on at $10/user/month on top of existing Notion plans. The Enterprise plan bundles it at around $20/user/month overall. Full pricing at notion.so/product/ai.

Best fit: Organizations whose primary knowledge repository is Notion and who want AI without integrating a separate search tool.

Honest gotcha: Notion AI Q&A does not search outside Notion. If your organization also uses Confluence, Jira, Slack, and Google Drive, you are getting partial coverage at best. The permission model is also Notion's workspace-level RBAC, not a fine-grained ACL sync - which is adequate for most Notion deployments but not equivalent to Glean's approach.

More: notion.so

8. Mem

Mem is best understood as an AI-powered personal and team knowledge base, positioned between a note-taking app and a light knowledge management system. Mem AI uses vector search across your notes collection to surface relevant context during writing and to answer natural language queries across your stored information.

What it does well: The frictionless capture experience is Mem's strongest point. Write a note and it is immediately searchable and surfaceable by the AI - no tagging, no folder organization required. For individual power users or small teams who want AI assistance without enterprise-scale infrastructure, it works well. The AI assistant can synthesize across notes to answer questions grounded in what you've captured.

Pricing: Mem is $14.99/user/month on the Pro plan. Team plans are available. Full pricing at mem.ai.

Best fit: Individual knowledge workers and small teams who want AI assistance over their personal notes collection. Not a fit for enterprise-scale multi-system search.

Honest gotcha: Mem is not an enterprise knowledge management tool in the Glean or Rovo sense. Connector support is limited, there is no real ACL-aware cross-system search, and the permission model is simple workspace access. Calling it an enterprise KM tool would be overselling it.

More: mem.ai

9. Coda AI

Coda occupies the intersection of wiki, spreadsheet, and application builder - and its AI layer, Coda AI, extends this into Q&A over your workspace content plus automation. Coda AI can summarize meeting notes, draft documents, answer questions based on your Coda tables and pages, and build interactive workflows.

What it does well: For organizations that have invested in Coda as their operational intelligence layer - tracking OKRs, managing projects, documenting processes - the AI integration is seamless. Coda AI can generate summaries from tables, answer questions about project status, and surface relevant docs based on context. The Pack system allows connections to external tools (Jira, Salesforce, GitHub, and ~300 others).

Pricing: Coda AI is included in the Pro plan at $30/user/month. Teams plan is lower at $10/user/month with more limited AI. Full pricing at coda.io/pricing.

Best fit: Organizations that have adopted Coda as their primary workspace tool and want AI built into that workflow rather than as a separate search layer.

Honest gotcha: Like Notion AI, Coda AI is strongest within the Coda ecosystem. Cross-system enterprise search is not the primary use case - the external integrations via Packs are functional but the search scope is limited compared to dedicated enterprise search platforms.

More: coda.io

10. Bloomfire AI

Bloomfire is a knowledge engagement platform with a long history in customer-facing and internal knowledge management, now enhanced with AI-powered search and Q&A. It is particularly common in customer support, sales enablement, and learning and development contexts.

What it does well: The content organization model in Bloomfire is strong. Content gets tagged, categorized, and linked in structured ways that give the AI better signal than raw unstructured document retrieval. The Q&A feature can leverage both the structured Bloomfire knowledge base and an underlying LLM to generate answers with source citations. Analytics on what questions employees ask and which answers they find (or don't find) are genuinely useful for content gap analysis.

Pricing: Custom pricing, contact required. Estimates from published reports suggest $25+/user/month for the AI-enhanced tier. Check bloomfire.com for current information.

Best fit: Organizations that want a combined knowledge base and AI Q&A system with strong content governance and analytics, particularly in support, sales, and HR contexts.

Honest gotcha: Bloomfire's connector coverage for external systems is narrower than Glean or Moveworks. It is a knowledge base tool with AI features, not a cross-system enterprise search platform. The two are different products serving related but distinct needs.

More: bloomfire.com

Notable Mentions

Slack AI Search adds AI-powered search natively within Slack. It can summarize channel conversations, find relevant messages, and surface context from threads. The permission model inherits Slack's channel access controls correctly. However, it only searches Slack - not your other systems. Available as an add-on to paid Slack plans. More: slack.com

Dashworks is a lean enterprise search product with clean connector coverage (~50 sources) and good permission-aware retrieval. It is positioned as a more affordable Glean alternative for mid-market companies. Pricing starts around $15/user/month. More: www.dashworks.ai

Unleash focuses on knowledge sharing and search with tight Slack and Teams integration. Used by mid-size engineering and product teams. Pricing from $8/user/month. More: www.unleash.so

Tana is an outlier in this list - it is a highly structured note-taking and personal knowledge management tool aimed at individuals who want hierarchical, link-first knowledge organization. The AI features help surface connections between notes and generate summaries. Not an enterprise search tool but powerful for individual knowledge workers. More: tana.inc

Qatalog provides an AI-powered work hub with cross-system search and knowledge management features. Backed by meaningful VC funding and used by mid-size engineering organizations. More: qatalog.com

Infrastructure Options - For Teams That Want to Build

Two tools exist outside the "buy a product" frame and merit separate treatment for organizations with engineering resources.

Vectara is an enterprise-grade managed RAG API. You index documents and users into Vectara's grounded generation pipeline, and it handles the retrieval - with per-user access controls baked into the retrieval API. A query scoped to a specific user only surfaces documents that user is authorized to see, enforced at retrieval time. This is the right architecture. A developer-focused free tier exists; production plans start at $300+/month. More: vectara.com

Stack AI is a no-code/low-code platform for building AI workflows, including RAG pipelines over your documents. You connect your sources, configure the retrieval pipeline, and deploy. ACL enforcement is what you implement - Stack AI gives you the plumbing, not the permission model. Pricing from $199/month for team plans. More: www.stack-ai.com

Cohere Command A is Cohere's enterprise LLM with a connector-based grounding API, deployable in your own VPC. For organizations that need LLM capabilities without data leaving their infrastructure, Cohere's enterprise deployment model is one of the more mature options. More: cohere.com/command

IBM watsonx Orchestrate targets large enterprises with complex automation needs, offering AI assistant capabilities over enterprise systems with on-premises deployment options. Part of a broader IBM AI portfolio. More: www.ibm.com/products/watsonx-orchestrate

Elastic AI Assistant brings conversational AI into the Elasticsearch/Kibana stack. For organizations already running Elastic for log analysis or operational observability, the AI Assistant adds natural language querying over their existing indexed data. The underlying RBAC is Elasticsearch's - correct permission inheritance for existing Elastic deployments. More: www.elastic.co/search-labs

The ACL Problem is Not Optional

Every enterprise AI search vendor claims their product "respects permissions." The claims are not equally meaningful.

There are at least three distinct approaches in the market, and they have very different security properties:

Real-time ACL sync (Glean's approach): At query time, the system checks whether the requesting user has access to each candidate document before including it in retrieval. Permission changes propagate in near-real-time. This is expensive to implement correctly but it is the only model that doesn't create permission-bypass windows.

Snapshot-based ACL filtering: The system syncs permissions periodically - hourly, nightly, or on a schedule. Documents are indexed with their permission state at sync time. There is a window between permission revocation and sync completion where a user might retrieve content they should no longer have access to. For most organizations this is acceptable risk. For regulated industries or high-stakes data, it may not be.

Post-retrieval LLM-based filtering: The system retrieves broadly and trusts the LLM to decline to answer questions about content the user shouldn't see. This is not a permission model - it is hope. I have seen multiple vendors who, when pressed, describe their security architecture in terms that amount to this. Do not accept it.

When evaluating any tool in this category, ask the vendor directly: "Walk me through what happens between the moment a user loses access to a Confluence space and the moment they can no longer retrieve content from that space via your tool." The answer to that question tells you more about the security architecture than any feature comparison table.

Best for X - Decision Matrix

Best for large enterprise, full stack, budget available: Glean. Best connector coverage, best permission model, designed for this use case.

Best for Microsoft-first organizations: Microsoft Copilot for M365. Native, permission-correct, covers the Microsoft surface area better than any third-party tool.

Best for Atlassian-first organizations: Atlassian Rovo. Deep Confluence and Jira integration with actionable agent capabilities.

Best for organizations with data residency requirements: Onyx (self-hosted) or Cohere Command A (VPC deploy). No SaaS vendor touching your data.

Best for customer support and sales enablement knowledge: Guru AI. Verified knowledge layer reduces wrong answers on high-stakes queries.

Best for Notion-centric teams: Notion AI Enterprise. Embedded, no setup, correct for the use case.

Best for building a custom solution: Vectara (permission-aware managed RAG) or Stack AI (build-your-own pipeline). Required engineering resources.

Best value for mid-market: Dashworks or Unleash. Reasonable connector coverage, correct permission handling, lower price points than Glean.

FAQ

What is the difference between enterprise AI search and a RAG system?

Enterprise AI search products (Glean, Rovo, Guru) are turnkey applications: they manage connectors, syncing, embedding, retrieval, and the user interface. A RAG system is the underlying architecture - retrieval-augmented generation. You can build an enterprise search product on top of a RAG architecture (Vectara and Stack AI expose this layer). Most enterprise teams are better served by a product than by building the infrastructure from scratch. For teams that want to build, see our RAG tools comparison.

Do these tools work with on-premises systems like SharePoint Server or older Confluence Server instances?

Most SaaS tools target cloud-hosted versions of these systems (SharePoint Online, Confluence Cloud). On-premises connector support varies significantly. Glean has on-prem connectors for some systems; Onyx's open-source model makes on-prem integration more accessible since you control the connector code. IBM watsonx Orchestrate and Elastic have the strongest on-premises support given their enterprise heritage.

How do these tools handle documents in non-English languages?

Multilingual support is increasingly standard for embedding models (multilingual e5, Cohere's multilingual embeddings, OpenAI text-embedding-3). Most tools in this list support multilingual content retrieval to varying degrees. Glean, Moveworks, and Microsoft Copilot explicitly claim multilingual support. Verify with a vendor whether semantic search quality holds for your specific languages, as quality varies by language and document type.

What about PII and sensitive data in the index?

This is the question vendors least want to answer clearly. Responsible tools offer PII detection during ingestion (Glean, Moveworks), configurable content exclusion rules, and audit logs showing what was retrieved in which query context. Ask specifically whether your tool indexes document content or just metadata - and what happens when a document containing salary data, personal health information, or customer PII gets indexed. Having admin-level controls to exclude certain content types or data sources from the index is a non-negotiable requirement for regulated industries.

Is there a free tier worth using for evaluation?

Glean offers enterprise trials but no true free tier. Guru has a free plan for up to 3 users. Onyx is free to self-host. Vectara has a free developer tier. Notion AI is available in a limited form on free Notion plans. For a genuine evaluation at small scale, Onyx self-hosted is the most cost-effective option - it gives you real ACL-aware enterprise search at zero license cost if you have someone to manage the deployment.

See also: /tools/best-ai-deep-research-tools-2026/ for tools focused on external web research rather than internal knowledge, and /tools/best-ai-rag-tools-2026/ for the underlying RAG infrastructure comparison.