Is Glean the context layer for enterprise AI?
For finding, inferring, and reasoning over enterprise context, Glean is a serious answer and a real product. The two solve different parts of the problem. Glean builds and selects context from connected systems and work signals. HexaHQ turns explicit teaching plus organizational evidence into recorded context about what AI should apply, including when the organization has not settled the answer. Plenty of companies may want both.
Glean and HexaHQ solve different parts of the context problem
Choose Glean when your main problem is finding and using information spread across a large enterprise. It has a mature indexing layer, a broad connector catalog, enterprise search, an Enterprise Graph, and a full agent platform.
Choose HexaHQ when AI needs the operating context people use to make decisions, including rules, corrections, exceptions, and conventions that may be duplicated, conflicting, implicit, or never formally documented. HexaHQ captures and infers that context, reconciles it, and makes the relevant version available wherever it applies.
What Glean does today, accurately
If you last looked at Glean as an enterprise search box, that picture is out of date. Its current platform describes 275 or more connectors across apps, documents, tickets, chat, and code, and an Enterprise Graph that captures the relationships between people, data, and activity, not only the content. It delivers that context scoped to each user's permissions. It has enterprise memory that learns continuously, and a per-user Personal Graph that models how each employee works by reading their activity across apps. Glean is not limited to what people wrote down on purpose. It infers from behavior too.
It is not locked to its own assistant either. Glean's MCP support feeds governed enterprise context into Claude Code, ChatGPT, Codex, Gemini, Cursor, and other front doors, and its AI Gateway matches each task to a model across 40 or more open and frontier models. Glean Protect covers security and governance, and Glean Agents automate work rather than only answering questions.
So the shorthand you will still find in third-party comparisons, "Glean searches, HexaHQ acts," is not a contrast we are going to make. It is no longer true, and arguing against a version of a competitor that stopped existing is a good way to lose a reader who has actually used the product.
Sources: Glean Platform; Glean connectors; Glean pricing; Glean Enterprise Context; Glean AI Gateway; Glean Personal Graph. All checked 28 August 2026. Glean's platform scope moves quickly, so check these before quoting this page.
Cases where we would not argue
You have thousands of employees and hundreds of thousands of documents, tickets, and threads, and the daily problem is that nobody can find anything. Retrieval across everything, scoped to each person's permissions, is exactly the right shape of answer, and it is a hard thing to build.
You want centralized administration of which models your company uses and what they cost. A gateway is the right tool for that, and HexaHQ does not do it.
You are buying for the whole company at once, with a procurement process, a security review, and an enterprise rollout plan. That is the motion Glean is built for.
Put plainly: if you need enterprise search across hundreds of systems, mature permission aware indexing, a large connector catalog, centralized AI administration, and a full agent platform, Glean is the stronger product today.
When the problem is judgment, not retrieval
If your problem is that AI needs company judgment and operating conventions that are scattered, implicit, conflicting, or undocumented, HexaHQ is the more direct fit. It turns explicit teaching and evidence from existing systems into reconciled company owned context that supported AI clients can apply consistently.
What happens after the evidence is found
The interesting difference is not retrieval against action, and it is not who can learn from behavior. Both products do that. It is what happens after the evidence is found.
Glean builds rich context from the systems where company information already lives. HexaHQ also captures the unwritten rules and corrections people teach AI while they work, then reconciles all of that evidence into machine usable organizational context: what is current, who it applies to, who has authority, and where the company itself still disagrees.
The distinction is not search versus action. Glean can feed context into AI and agents, take actions, work across AI surfaces, and support custom actions. The HexaHQ distinction is the step between evidence and consistent organizational behavior: turning documented and undocumented evidence into the resolved context AI should actually apply.
The reason that matters is that much of what AI needs is tacit operating knowledge. Nobody writes a handbook page explaining how this company uses a particular Salesforce field. People learn it by doing the work, watching experienced teammates, and being corrected. Search cannot retrieve a policy document that does not exist.
Even rich enterprise context still leaves a separate question: when evidence overlaps, conflicts, or was never formally documented, what should the AI consistently believe and do? HexaHQ is designed around resolving that question.
HexaHQ's unit is the organizational concept the AI should actually apply: the current rule, correction, preference, exception, or process, plus where it applies.
Three consequences follow, and they are the ones worth testing in a trial rather than in a feature grid.
Context can be taught as well as found
When an AI gets something wrong and you tell it so, that is the highest-value moment in the whole system, and in most setups it evaporates when the chat ends. HexaHQ can keep that correction, and it can also infer supporting evidence from prior work in your systems. Both are treated as evidence rather than as an instruction to rewrite company policy.
Reconciliation decides what the evidence means
Finding relevant evidence is not enough when the evidence overlaps or disagrees. HexaHQ resolves whether two statements describe the same concept, whether one supersedes another, whether both remain valid for different audiences, or whether the organization itself has not settled the question. That is how the same concept can produce the same intended behavior wherever it applies.
Applicability is stated, not implied
"Applies to enterprise renewals, above $50,000, since the March pricing change" is written with the rule, so the AI can tell whether the rule binds the deal in front of it. Prose written for human readers leaves that in the reader's head.
The Salesforce rule that is not in any document
A sales manager expects reps to update both Next Step and Next Step Date whenever an opportunity changes. Nobody wrote a policy document about those two Salesforce fields. The rule exists because experienced people know it, reinforce it, and correct it when someone gets it wrong.
Enterprise search cannot retrieve a document that does not exist. HexaHQ can capture the correction, infer supporting evidence from prior work, reconcile it with related guidance, and make the resulting rule available whenever the same situation occurs.
Reconciliation is not cleanup. It is what makes AI consistent.
It is tempting to read reconciliation as administrative tidying, a chore somebody does to keep a knowledge base neat. It is the opposite. Reconciliation is the step that lets an organization get the same answer twice.
If ten people work in five different AI clients, the organization should not get ten independent interpretations of the same operating rule. The same underlying concept should resolve to the same intended meaning wherever it applies. Getting there means resolving duplicates, stale versions, overlapping concepts, supersession, authority, audience, applicability, and legitimate differences between teams.
It also means leaving genuine disagreement visible. When two current rules conflict and the company has not decided which one governs, the honest runtime context says so, and the AI can ask a person or avoid an irreversible action rather than quietly picking the higher ranked document.
A source is evidence. It is not automatically the rule.
A Google Doc, a Slack message, an email, or a CRM record is evidence about how a company works. It is not necessarily the context AI should apply. One source may carry several concepts at once, be out of date, conflict with another source, contain an exception, apply to one team only, have no clear owner, or leave out the actual operating convention entirely.
What AI needs at runtime is smaller than the source corpus, not larger: the concept, its current meaning, who has authority over it, who it reaches, and where it applies. The source stays valuable as provenance. The resolved concept becomes the thing the AI reasons from.
That shrinks token use, and token use is the least interesting part of it. The reason to do it is consistent decisions. Ten AI clients reinterpreting the same pile of documents will sometimes agree and sometimes not. Company knowledge in HexaHQ goes further into how the resolved layer sits beside the systems you already have.
Where each one is pointed
| Glean | HexaHQ | |
|---|---|---|
| Search and index across source systems | Yes, 275+ connectors and an Enterprise Graph | No. HexaHQ is not a search index |
| Context into other AI clients over MCP | Yes | Yes |
| Central model choice and cost control | Yes, a gateway across 40+ models | No. You keep the AI client you already use |
| Where the context comes from | Indexed from your systems, plus graphs inferred from activity | Taught by people, inferred from your systems, then reconciled into one concept |
| A rule nobody ever wrote down | Its graphs and memory can infer patterns from how people work | Captured or inferred, then reconciled into a reviewable rule with an owner and an audience |
| Two versions of the same rule | Signals such as freshness, authority, and usage pick the stronger candidate at retrieval | A recorded decision: superseded, kept valid per audience, or left explicitly unresolved |
| Who receives which context | Scoped to each user's permissions | Audience and applicability are stored with the reconciled context, including external sharing where allowed |
| Custom actions against your own API | Yes, custom actions from an OpenAPI spec, plus MCP tools and connectors | Yes, described in conversation and authored by the AI, with no server to host |
| How you start | Book a demo | One person, free, today |
Shared context is not a single company-wide pile
Both systems can respect access boundaries. HexaHQ's additional question is whether a piece of canonical context is meant to apply to this person and this task in the first place. Glean scopes retrieval to what each user is permitted to see in the underlying systems, which is the right answer for indexed material. In HexaHQ audience is a property of the reconciled concept itself: personal, a named team or project group, the whole organization, or a specific external collaborator such as a client, contractor, or advisor.
The reason that matters is that a lot of the most useful organizational context does not correspond to a file anywhere, so there is no source permission to inherit. Someone has to say who it is for. A person's AI then inherits that same boundary: it can use what its person is meant to have, plus that person's own working context, and it does not quietly acquire the rest.
When two teams are both right
Two sales teams may use the same Salesforce field differently. One treats Next Step Date as the next customer interaction. The other uses it for the next internal milestone. Both conventions are real, both are enforced by the people on that team, and neither is written down.
The right answer is not necessarily to pick one company-wide rule. HexaHQ can keep both versions when they are intentionally different and resolve the right one for each team. Each rep's AI then behaves the way that rep's team expects, from one shared concept.
Glean and HexaHQ, answered
Is Glean the context layer for enterprise AI?
For finding, inferring, and reasoning over enterprise context, it is a serious answer. It indexes many source systems, infers from work signals, respects each user's permissions, feeds context to other AI clients over MCP, and runs agents that take actions. The two solve different parts of the problem. Glean builds and selects enterprise context from connected systems and work signals. HexaHQ turns explicit teaching plus organizational evidence into recorded context about what AI should apply, including when the organization has not settled the answer.
Does Glean only do search?
No, and any comparison that says so is out of date. Glean's current platform covers enterprise context and an Enterprise Graph, enterprise memory, a large connector catalog, MCP access into several AI front doors, a gateway across many models, governance, and agents that take actions.
What is the difference between finding a source and reconciling context?
Finding a source hands the AI relevant material and leaves the interpretation to it. Reconciling context decides what that material means before the AI uses it: whether two statements describe the same concept, which one is current, who has authority, who it applies to, and whether the company has actually settled the question. Much of the operating knowledge a company runs on was never written down at all, so it has to be taught or inferred first, and then reconciled like any other evidence.
Can HexaHQ and Glean be used together?
Yes. They solve adjacent problems. A company can keep an enterprise search layer over its documents and still want one place where the settled rules, corrections, and reusable ways of working are reconciled, scoped to an audience, and connected to real actions.
Is model neutrality a difference between the two?
Not on its own. Glean offers context through MCP to several AI front doors and a gateway across many models, so working across models is not unique to HexaHQ. The argument that does differ is ownership: the reconciled context your organization builds in HexaHQ, its knowledge, workflows, and capability definitions, belong to the company rather than to one model vendor. More on that in whether ChatGPT and Claude can share memory.
Can I try HexaHQ without talking to sales?
Yes. HexaHQ has a free tier and one person can start today by connecting their own AI client. Glean does not publish pricing and its site directs prospects to book a demo, which suits a company-wide rollout and is a slower path if you want to test something this afternoon.
What about token efficiency?
Both make this argument, and it is supporting proof rather than the reason to choose either one. Distilled context does cut retrieval, tokens, and latency, because the model stops re-reading long documents to recover the same rule. The durable benefit is that irrelevant context degrades reasoning even when tokens are cheap.
Related comparisons
Try HexaHQ on a rule your team keeps repeating.
Start with one person, one supported AI client, and one operating rule that normally lives in someone's head. See whether the next related task starts with the right context.