Glean

Most AI assistants are smart about the open web and blind to the company paying for them. Glean built the opposite.

What Glean actually does

Glean is AI search and automation that sits on top of a company's own information. It connects to more than 100 of the apps a business already uses (Slack, Google Drive, Confluence, Jira, Salesforce, SharePoint, GitHub) and builds a map of how that company's documents, people, conversations, and projects relate to each other. On that map an employee can search across everything at once, ask a question and get an answer assembled from many systems, or hand off a multi-step task to an agent (AI that completes a job on its own, not just answers a question). The older way meant pinging three coworkers and digging through four tools to reconstruct what the company already knew. Glean moves that first hour of hunting into one query. Crucially, it mirrors each person's permissions, so it only ever surfaces what that employee is already allowed to open. The model doing the talking is interchangeable; the durable product is the connected, permission-aware map underneath it.

The analogy

The closest comparison is the Bloomberg Terminal, and the parallel is specific. Before Bloomberg, financial data sat scattered across exchanges, brokers, and news wires, gated and hard to assemble. Bloomberg consolidated it behind one permissioned screen that became where professionals start the day. The value was never any single feed. It was the consolidation plus the ranking of what mattered. Glean runs the same play on a company's internal knowledge: hundreds of disconnected apps pulled into one ranked surface, access controls intact, that becomes the default place work begins. The mechanism that transfers is consolidation of fragmented, permission-gated information into a single starting point, not a chatbot bolted onto one app.

Who they serve

Glean sells to large enterprises with knowledge sprawled across many tools. Named customers span travel (Booking.com), media (TIME), telecom (Ericsson), fintech (GCash), real estate (Zillow), and a long logo wall of technology companies (Samsung, Databricks, Reddit, Pinterest, Intuit, Webflow). The buyer is usually a CIO, head of IT, or a digital-transformation lead who owns the company's AI rollout and answers for its security. The official target and the paying base look aligned: Glean markets to large, multi-tool organizations, and that's who shows up in the customer list. The common thread is a company whose knowledge lives in dozens or hundreds of systems and no single suite can reach all of it.

Who shouldn't use it

A small company that runs almost entirely inside one suite, all Microsoft 365 or all Google Workspace, where the native assistant (Copilot or Gemini) already reaches most of the data. There the bundled tool is cheaper and the connector breadth matters less. A team that only needs a chatbot over a handful of documents will find a lighter tool like Notion AI or Guru a better fit. A team whose knowledge isn't fragmented, with few apps and tidy access, gets less from Glean's main convenience, which is unifying sprawl. And a budget-constrained small team should expect a sales-led, enterprise-priced motion: Glean publishes no per-seat pricing, which usually signals higher commitment and a buying process, not a card swipe this afternoon.

Sample customer stories

Real customer: Booking.com. The travel company rolled Glean out as its first AI platform adopted company-wide, across roughly 14,000 employees. The concrete win is breadth of trust: instead of a contained pilot, teams across the company began using one tool to find answers and turn everyday work into AI workflows, because the permission model meant IT could open it to everyone at once rather than gating it to a safe department.

Real customer: TIME. The magazine had more than a century of archives locked across systems. With Glean, its sales and editorial teams can search 100-plus years of content as working knowledge, and the company reports going live in about three weeks. The win is the thing generic AI can't do: make a specific company's private, messy, decades-deep corpus searchable and usable, not just answer general questions well.

What only Glean can claim

Glean is built around a permission-aware map of one company's knowledge, and it stays deliberately neutral about which AI model runs on top.

The map is the engine. Glean's knowledge graph learns how a company's people, documents, and activity connect, and it mirrors each employee's permissions so search and agents only ever surface what that person can already open. Why a buyer cares: you can deploy AI to the whole company without it exposing HR files or M&A documents to the wrong people.

It sees the whole company, not a slice. Glean connects more than 100 SaaS apps and data repositories. The payoff: answers are assembled from everywhere the work actually lives, instead of one vendor's corner of it.

It gets sharper the more it's used. Because relevance draws on how people across the company search and work, the ranking of what matters improves as adoption grows. The longer Glean runs inside a company, the better it gets at that company specifically.

It refuses to lock you in. Glean supports 35-plus AI models, exposes open APIs, and states it never walls your data in. So a buyer isn't wagering the company on a single model staying ahead, and can feed Glean's context to other AI efforts too.

The pedigree is real. Founder Arvind Jain spent about a decade building Google's search before co-founding the data company Rubrik and then Glean. The ranking expertise behind the product is the genuine article, aimed at the private, permission-gated data Google can't index.

Breadth is optional, not a bundle. Search, the assistant, and agents each stand on their own, so a company can start with search and add agents as trust grows.

Why this is hard, and why it matters now

Durable structural shift: general-purpose AI only crossed the reliability bar for real work in 2023, and agents that run multi-step tasks landed across 2024 and 2025. But once the model got good, the bottleneck moved. A model is only as useful as the company-specific context you can safely feed it, and most enterprises have their knowledge trapped across hundreds of apps with conflicting permissions. Untangling that, while respecting who's allowed to see what, is the actual hard problem, and it's exactly what a generic chatbot skips. There's a governance shift too: as companies deploy AI to everyone, "it surfaced something it shouldn't have" became a board-level risk, which makes permission-aware access a requirement, not a feature.

Current shift: model costs are climbing as AI touches more workflows, so feeding an AI only the relevant company context, rather than dumping everything at it, now shows up as real money. Glean leans on that, citing lower token use than off-the-shelf approaches. The category exists now because the models got good, the context problem got urgent, and the cost of getting it wrong got visible.

What people would use instead

Without Glean, the work falls back to the old hunt: messaging coworkers and opening one tool after another to reassemble what the company already knew. The next fallback is each app's own built-in AI, which is fluent inside its own walls but can't see across them, so the answer is only ever as complete as one system. The third is a general assistant like ChatGPT, which drafts well but has no view of the company's private knowledge, no cross-app permissions model, and raises confidentiality questions a regulated enterprise can't wave away.

Competition

• Microsoft 365 Copilot, built around the Microsoft 365 estate and the data inside it.
• Google Gemini Enterprise (Agentspace), built around Google Workspace and Google's cloud AI.
• ChatGPT Enterprise and Claude Enterprise, built around frontier models with their own growing set of connectors.
• Enterprise-knowledge and search players: Guru, Dust, Coveo, Elastic, and newer entrants like Sana and Dashworks.

Copilot and Gemini are built around a single vendor's suite and reach outside it through add-on connectors; Glean is built around being model-neutral and app-neutral, with a permission-aware knowledge graph spanning third-party tools as the core. The sharpest dividing line is the heterogeneous company: an organization whose knowledge lives across Slack, Confluence, Jira, Salesforce, GitHub, and Google Drive alongside Microsoft 365 is where Glean's breadth is the point, and that describes most large enterprises.

Website analysis

The hero is doing real work; the subhead buries the moat. "Glean connects knowledge, systems, and context so AI can actually work" names the specific thing and lands a quiet edge with "actually work." Then the subhead spends six claims on category boilerplate every enterprise AI vendor says: "trusted decisions, cross-system execution, safe, scalable AI, ship work faster, reclaim thousands of hours, prove ROI." Nothing in there is Glean's alone. The two things competitors genuinely can't copy overnight, the permission-aware knowledge graph and the model-neutral, no-lock-in stance, are softened into the single word "context" and the phrase "safe, scalable AI." Both have dedicated product pages (System of Context, Enterprise Graph) but don't reach the homepage in plain terms. This is a buried-moat finding. A buyer scanning the page sees another outcomes-and-ROI pitch. They don't see the permission model that lets them deploy to everyone, which is the actual reason to choose Glean over the suite assistant they already pay for.

Website rewrite

This is a hero-good, subhead-weak case. The element-level audit:

• Current hero (verbatim): "Glean connects knowledge, systems, and context so AI can actually work."
• Current subhead (verbatim): "Enterprise AI that runs on outcomes. Turn company knowledge into trusted decisions, cross-system execution, and safe, scalable AI — so teams ship work faster, reclaim thousands of hours, and prove ROI."
• Current CTA (verbatim): "Get a Demo"
• Rewritten hero: Keep as is. It names the specific thing, is distinctive, and the "actually work" turn does honest work against blander rivals. Rewriting it would trade earned equity for nothing.
• Rewritten subhead: "It reads across 100+ of your work apps and only ever shows each person what they're already allowed to see, so you can give the whole company AI without leaking what shouldn't be seen."
• Rewritten CTA: Keep as is. "Get a Demo" matches an enterprise sales motion with no public pricing.
• Reasoning: the original subhead lists six benefits a competitor could claim word for word; the rewrite swaps that for the two facts only Glean can state, connector breadth and permission-aware access, and pairs with the demo CTA rather than echoing it. This is a positioning hero, not an awareness one: the homepage's intended visitor, a CIO or transformation lead, already knows who Glean is, so the hero claims the position and the subhead carries the proof.

Messaging to consider

A gift to the team: three lines that are Glean's alone to say.

1. Built by the people who built Google search, for the data Google can't see.
2. AI that can see your whole company, not just one vendor's corner of it.
3. Give everyone AI, and it still only shows each person what they can already open.

Lead with the first. It passes ownability most cleanly: only Glean's founders built Google's search and then turned that craft on private enterprise data, the claim needs no setup, and the buyer payoff (search that works on your messy internal knowledge the way Google works on the web) is legible on sight. The second re-categorizes Glean out of the "single-vendor assistant" box; the third names the permission model as a buyer outcome.

Likely next questions a prospect would have

• How is Glean priced, per seat or by usage, and what's the floor? (No public pricing.)
• How exactly does permission-mirroring work, and what stops the AI from surfacing something a user shouldn't see?
• How long is deployment, and does Glean connect the specific apps we run?
• Which models can we use, and is our data ever used to train them?
• How do we govern thousands of agents once teams start building them?
• How accurate is it on our internal content, and how are wrong answers handled?

Sources

Company sources: https://www.glean.com/ , https://www.glean.com/blog/glean-series-f-announcement , https://www.glean.com/press/glean-achieves-100m-arr-in-three-years-delivering-true-ai-roi-to-the-enterprise , https://www.glean.com/product/system-of-context , https://www.glean.com/product/enterprise-graph , https://www.glean.com/resources/customer-stories/booking-com , https://www.glean.com/resources/customer-stories/time , https://www.glean.com/security , https://www.glean.com/about

Third-party sources: https://www.cnbc.com/2025/06/10/glean-gen-ai-search-startup-raises-150-million-at-7-billion-value.html , https://news.crunchbase.com/venture/ai-powered-work-assistant-glean-valuation-jumps/ , https://sacra.com/c/glean/ , https://futurumgroup.com/insights/glean-doubles-arr-to-200m-can-its-knowledge-graph-beat-copilot/ , https://sequoiacap.com/article/arvind-jain-glean-spotlight/ , https://www.fastcompany.com/91406548/glean-ceo-arvind-jain-interview