AI knowledge tools: turning company docs into something agents can use

Your company has accumulated years of institutional knowledge across Notion, Slack, Google Drive, Confluence, and a half-dozen other SaaS tools. Your new AI agents know nothing about any of it. Fixing that gap — turning your scattered docs into something an agent can reliably retrieve and act on — is now its own product category, and the stakes are higher than most teams realize.

What “enterprise search” actually means now

The label is a decade old, but the product has fundamentally changed. RAG transforms internal search from keyword matching to semantic understanding — employees ask questions and receive synthesized, cited answers from across the entire organizational knowledge base. That’s the consumer-facing story. The more important shift is what happens when agents, not humans, are the ones doing the querying.

Glean, for example, provides enterprise search, knowledge discovery, and AI-driven insights across workplace systems — including collaboration tools, documents, and internal applications — combining search, generative AI experiences, and automation into a unified enterprise knowledge layer. The framing has moved from “help your employees find stuff faster” to “give your agents a grounded, permissioned knowledge runtime.” Those are very different design requirements.

The three tools operators are evaluating

Glean is the incumbent in this space and the natural first call for large orgs. Their goal is to solve a real problem: company knowledge is scattered everywhere, making it a nightmare to find anything — and Glean promises to fix this with a single, smart search bar for your entire digital workplace. The tradeoff is cost and opacity. Pricing in 2026 starts at approximately $50+ per user/month, with a minimum enterprise contract of around 100 seats (~$60,000/year ACV). Glean does not publish standard pricing — all contracts are custom-quoted through a direct sales process — and buyer-reported enterprise contracts can exceed $200,000 annually.

Glean’s total cost of ownership extends well beyond the per-seat license; fully loaded annual TCO for mid-to-large deployments typically ranges from $350,000 to $480,000 when all cost layers are modeled together.

Dust positions itself at the operator layer rather than the boardroom. It transforms scattered institutional knowledge into an interactive intelligence layer, connecting AI agents to company data across 45+ tools with retrieval quality that consistently outperforms generic chatbots.

The no-code builder and model-agnostic architecture let teams deploy specialized agents without engineering overhead, while SOC 2 Type II certification and granular access controls satisfy enterprise security requirements. Pricing is more transparent: the Pro tier at $29/user monthly is the entry point — full model access, unlimited agents, connections to all 45+ data sources, and unlimited messages under fair use limits.

Enterprise pricing requires 100+ users but adds the SSO, SCIM, and compliance features that unblock procurement for larger organizations. Dust also supports the Model Context Protocol (MCP) to connect agents to external tools , which matters if you’re building custom workflows on top. The platform recently closed a $40M Series B and now serves more than 3,000 organizations, with 51,000 monthly active users and 300,000 agents deployed across the platform.

Sana started as an AI-native learning management system and has expanded into broader knowledge-agent territory. As of September 2025, Sana was acquired by Workday , which signals where enterprise HR and knowledge platforms are converging. The platform positions itself as one hub for creating expert AI agents grounded in your company’s knowledge, starting from $0/month on its Agents product. Sana Agents reads documents (PDF, PowerPoint, Word, Docs), structured files (Excel, Sheets, CSV), and video (MP4), and connects to third-party systems such as CRMs. The Workday acquisition makes it worth watching if you’re already embedded in the Workday ecosystem, but it also introduces the usual enterprise acquisition caveats around roadmap continuity.

The permissions problem nobody talks about enough

Here’s the issue that separates serious knowledge infrastructure from a demo that looks good on day one: the permission logic that lived in SharePoint, Drive, or Confluence does not travel with the chunk. By the time the LLM sees the retrieved context, it has no idea that the original document was restricted to a specific department, project, or named user list.

This isn’t theoretical. In late 2025, the EchoLeak vulnerability demonstrated how attackers could use a specially crafted email to manipulate Microsoft 365 Copilot’s enterprise RAG pipeline, tricking the AI into retrieving and exfiltrating sensitive corporate data without any employee interaction.

In the 2025 OWASP Top 10 for LLM Applications, Sensitive Information Disclosure jumped from #6 to #2, and Vector and Embedding Weaknesses was added as a brand-new category.

The right architecture enforces permissions at retrieval time, not just at ingestion. The good platforms inherit access controls from the source system at retrieval time, then filter candidate chunks before passing context to the LLM. When you’re evaluating any tool in this class, that’s the question to ask in the demo: where exactly in the pipeline do you enforce document-level ACLs?

Grounding: the thing your agents actually need

“Grounding” is the practice of making an agent’s outputs traceable back to specific, authoritative internal sources — so it doesn’t hallucinate a policy that doesn’t exist or cite a procedure that was deprecated two quarters ago. Successful enterprise agent deployment depends on grounding outputs in verified enterprise knowledge, enforcing least-privilege permissions, validating inputs and outputs, and maintaining auditability through logging and review.

For operators building on top of something like Relevance AI or n8n, the practical question is whether you want a managed knowledge layer (Glean, Dust, Sana) or whether you’re assembling your own RAG stack. The market has split into three layers: turnkey RAG platforms, cloud RAG services tied to a hyperscaler, and RAG infrastructure or frameworks you assemble yourself. Turnkey saves you months of infrastructure work; DIY gives you more control over chunking strategies, embedding models, and cost. Most operators under 200 seats should default to turnkey until they have a compelling reason to build their own retrieval layer.

One signal that the category is maturing: key metrics suggest 85% of enterprise AI applications now use RAG as foundational architecture , up significantly from prior years. This is no longer an advanced pattern — it’s table stakes.

What to look for when you’re buying

Before signing anything in this category, pressure-test these five things:

1. Connector freshness — How often does the index sync? Stale docs are worse than no docs for agents.
2. Permission inheritance — Does it enforce source-system ACLs at query time, or only at ingestion?
3. Citation and traceability — Can every agent response be traced back to a specific document version?
4. Model agnosticism — Are you locked into one LLM, or can you swap as frontier models improve?
5. Total cost of ownership — License, infrastructure, implementation, and support. Organizations that evaluate only the headline per-seat rate routinely face significant budget surprises at renewal. Modeling full TCO before signing is essential.

For teams that want to explore how these tools compare on specific workflows, the Mem and Notion AI comparison at mem-vs-notion-ai covers some of the lighter-weight end of the knowledge management spectrum, while Dust sits firmly in the agent-infrastructure tier.

---

Bottom line: The Glean/Dust/Sana class of tools has moved well beyond search into genuine agent infrastructure — but the gap between a good demo and a production-ready system comes down to permissions and retrieval architecture, not the feature list. Start with a managed layer unless you have dedicated ML infra capacity, and never treat the per-seat price as the real number.