Glean vs. UnifyApps: Enterprise AI Search and Context Graph Comparison

Glean and UnifyApps both build a graph that gives AI context about your business, but they answer a different question. Glean answers: what does your company know, and who's allowed to see it? UnifyApps answers: what is your business, how does it work, and what is the AI allowed to do?

Both matter, even though they're not the same question. An AI agent that can't tell whether two systems are referring to the same vendor, or doesn't know which rule applies before acting on a record, ends up guessing rather than reasoning. Glean and UnifyApps both address that problem, but they take different approaches to who should own the context layer and what it enables.

That question is also an architectural one. Cloud infrastructure and systems of record are layers most enterprises have already settled on, and AI models are becoming interchangeable. The layer that operationalizes enterprise intelligence on top of them is not yet standardized. That is the decision Glean and UnifyApps represent, in different ways.  

Glean treats its Enterprise Graph as core infrastructure inside its own products. The company describes it as an intelligence layer that builds a dynamic understanding of how work happens across the enterprise, invisible to users and working behind the scenes to power Glean's own Search, Assistant, and Agents. It combines company-wide knowledge with a personal graph for each employee. The graph captures relationships between people, content, workflows, and applications to help Glean tailor search and chat across the 100+ apps it connects to.

UnifyApps takes a more enterprise-owned approach. Your team defines the categories and relationships that make up the graph, edits them as the business changes, and stores the result inside your own cloud, VPC, or on-prem. The graph itself draws from a catalog of 1,000+ enterprise connectors to pull in data from across your organization. It blends structured data such as CRM records with unstructured content such as call transcripts into a single graph, so an agent can pull from both without stitching two systems together after the fact. Because your team defines and owns that graph, it moves with you if you ever change platforms. 

What each graph models

Glean's graph connects people, content, applications, workflows, and permissions across the enterprise. Those relationships help Glean understand how information is related and give its Search, Assistant, and Agent products more context when answering questions and completing tasks. It answers questions like "who worked on this deal" or "where does this document live."

UnifyApps' graph models the business entities themselves, such as vendors, contracts, invoices, customers, employees, and the relationships between them, defined and versioned the way your company understands them. It answers questions like "is this the same vendor we already have a contract with" or "what's this invoice waiting on before it can be approved."

Glean's graph provides AI with a rich understanding of how information and activity connect across the enterprise. UnifyApps makes the business model itself explicit so AI has a consistent way to reason about entities, relationships, and processes across systems. Because those definitions are shared across systems, the same business context can be used by different agents and workflows without each one having to interpret the underlying data separately.

Who defines the graph, and why it matters

With UnifyApps, your team defines the business model directly—what a vendor is, what a contract is, how they relate, and what counts as one entity versus two—and that structure is the ontology your agents reason against. Every node, edge, and property stays visible and editable, and any conclusion an agent reaches traces back to the record it came from.

That traceability is the difference between an AI system someone can audit and one they have to trust blindly. When an agent draws on a structured, governed graph instead of raw text spread across systems, its answers are grounded in specific, checkable records rather than inference. 

If an agent says a vendor's payment terms changed, someone can open the exact record that change came from, see when it was updated and by whom, and confirm the agent didn't misread a stale document or merge two similar vendors into one. 

In a regulated industry, that kind of traceability can be the difference between an AI pilot and a production system because teams need to be able to verify where an agent got its information and why it reached a particular conclusion.

Where the platforms diverge beyond the graph

Getting information into a graph is one thing. Making sure that information is accurate, connected, and ready for AI is another. Both Glean and UnifyApps bring information from across the enterprise into a connected context layer, but they take different approaches to the data that feeds it.

Glean connects to enterprise applications and crawls their content, metadata, permissions, and activity, then indexes that information for use across its products. The graph provides a connected view of information across the enterprise and makes it easier for AI to find relevant context.

UnifyApps treats the underlying data as something to actively prepare and manage. Its pipelines move and transform data across systems, while matching and survivorship rules reconcile records for the same entity and resolve conflicts between sources. Lineage traces those values back to their origins, so teams can see where the information came from and how it was assembled.

Data preparation matters when the AI needs to act, not just find. An employee might locate a signed vendor contract in seconds. But acting on it could still require updating the ERP, routing an approval, and notifying other teams.

To handle those steps correctly, the AI needs to know that the vendor in the contract is the same vendor in the ERP, which terms are current, and which policies govern the change. That's where reliable automation depends on understanding the business entities, policies, and relationships behind the information.

Beyond search: what the context graph enables: Glean vs. UnifyApps 

Glean's graph tells an agent what your company knows and who's allowed to see it. But knowing a vendor exists and who can view its file doesn't tell an agent whether it's allowed to change that vendor's terms, what approval that change requires, or what to do next. For an agent to take that next step, it needs the business rules and processes that govern the information it has found.

UnifyApps brings these pieces together in the Enterprise Brain, which combines knowledge, governance, and actionability in the same context layer. Knowledge is the same consistent understanding of the business covered above, with a vendor recognized as the same vendor everywhere it appears. Governance attaches a policy to that vendor once, so every agent that touches the record inherits the same rule instead of having it rebuilt. Actionability means the AI can carry out the next step itself once it knows what’s allowed, instead of stopping at a description of what should happen.

Think of this the way a car works: the engine is essential, but the transmission, safety systems, and navigation are what make it drivable. Glean’s graph is a powerful navigation layer. The Enterprise Brain is what connects the navigation to the controls, so the car can act on where it’s going. 

Glean’s agents can take actions within the permissions of the source app, such as updating a Salesforce opportunity or creating a Jira ticket. What an agent working off the Enterprise Brain adds is the business rule: it can confirm the renewal terms, check that they fall within policy, and route the update for the right approval, because the knowledge, the rule, and the ability to act on it are connected in the same context layer. Knowing what's in the contract is the first step. The Enterprise Brain handles what happens next. 

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Comparing Glean and UnifyApps