Uvi vs Glean
Glean makes enterprise search smarter for humans. Uvi is the reasoning layer for enterprise AI agents - built on a Decision Graph that returns the chain instead of a list of links. The two solve different problems - here's the full picture.
TL;DR
Glean indexes documents across SaaS apps and returns the most relevant links plus a generated summary. Uvi indexes and joins the same systems, then encodes the chain of decisions your team makes on top - so an AI agent gets the structured chain it needs to act, not a list of documents to read. Search returns links. The reasoning layer returns the chain.
The Core Difference
Enterprise search platforms like Glean were built for the knowledge worker problem: a person sits down, has a question (“what is our refund policy,” “how do I expense a flight,” “where is the design doc for feature X”), and the answer almost always lives inside a single document somewhere in the company's SaaS stack. Glean indexes that stack, ranks the documents, and renders a summary on top. For that workload it is excellent.
AI agents have a fundamentally different problem. The questions they need to answer often have no single source document - “which features shipped behind feature flags last week,” “what is blocking the checkout deploy,” “which engineers own the failing service.” The answer is a structure that must be assembled by joining many systems through your organization's private definitions of what a feature, a deploy, or a service even is.
Search cannot reach those answers, because the joins it would need are not written down anywhere. Uvi's answer is to build a model - an enterprise knowledge graph - that an agent can query directly.
Feature-by-Feature Comparison
Primary user
- Uvi
- AI agents executing tasks across systems
- Glean
- Knowledge workers searching for documents
Retrieval output
- Uvi
- Structured answer queryable in GraphQL or via MCP
- Glean
- Ranked list of documents + generated summary
Underlying model
- Uvi
- Two-tier knowledge graph: topic graph (cross-tool links) + enterprise graph (your company's private definitions)
- Glean
- Centralized search index across SaaS connectors
Cross-system understanding
- Uvi
- Joins entities across tools (one person, one ticket, one feature) and follows implicit references (branch names, flag keys, code mentions)
- Glean
- Surfaces documents from multiple SaaS apps
Org-specific semantics
- Uvi
- Yes - Forward Deployed Agent encodes your team's private definitions of features, deploys, customers, etc.
- Glean
- No - generic search ranking
Deployment
- Uvi
- Full on-prem / VPC, air-gapped - zero data egress
- Glean
- Multi-tenant SaaS, single-tenant private cloud option
Security model
- Uvi
- Document-level RBAC enforced on every node of the graph at retrieval time - data never leaves your environment
- Glean
- Honors source-system permissions at retrieval
Data sources
- Uvi
- GitHub, GitLab, Jira, Linear, Confluence, Notion, Slack, Datadog, Splunk, Postgres, and internal/custom systems via the Forward Deployed Agent
- Glean
- 100+ SaaS connectors - Google Workspace, Slack, Confluence, Jira, GitHub, Salesforce, and more
Best workload
- Uvi
- AI agents in R&D and operations - when the answer must be joined from many systems using your company's own definitions
- Glean
- Horizontal knowledge search for IT, HR, sales - when the answer lives inside a single document
| Feature | Uvi | Glean |
|---|---|---|
| Primary user | AI agents executing tasks across systems | Knowledge workers searching for documents |
| Retrieval output | Structured answer queryable in GraphQL or via MCP | Ranked list of documents + generated summary |
| Underlying model | Two-tier knowledge graph: topic graph (cross-tool links) + enterprise graph (your company's private definitions) | Centralized search index across SaaS connectors |
| Cross-system understanding | Joins entities across tools (one person, one ticket, one feature) and follows implicit references (branch names, flag keys, code mentions) | Surfaces documents from multiple SaaS apps |
| Org-specific semantics | Yes - Forward Deployed Agent encodes your team's private definitions of features, deploys, customers, etc. | No - generic search ranking |
| Deployment | Full on-prem / VPC, air-gapped - zero data egress | Multi-tenant SaaS, single-tenant private cloud option |
| Security model | Document-level RBAC enforced on every node of the graph at retrieval time - data never leaves your environment | Honors source-system permissions at retrieval |
| Data sources | GitHub, GitLab, Jira, Linear, Confluence, Notion, Slack, Datadog, Splunk, Postgres, and internal/custom systems via the Forward Deployed Agent | 100+ SaaS connectors - Google Workspace, Slack, Confluence, Jira, GitHub, Salesforce, and more |
| Best workload | AI agents in R&D and operations - when the answer must be joined from many systems using your company's own definitions | Horizontal knowledge search for IT, HR, sales - when the answer lives inside a single document |
Architecture: How They Differ
Glean Architecture
- 01SaaS connectors crawl 100+ source systems
- 02Documents are indexed into Glean's centralized search index
- 03At query time, a hybrid ranker returns the top-K relevant documents
- 04An LLM summarizes the top results into a generated answer
- 05Source-system permissions are honored at retrieval
Result: Smarter search. Documents and a summary. Still ultimately a retrieval over a centralized index.
Uvi Architecture
- 01Connects to engineering and operational systems (code, tickets, PRs, docs, Slack, monitoring) and indexes the entire dataset
- 02Builds the topic graph - cross-tool links + entity resolution + document-level permissions on every node
- 03A Forward Deployed Agent encodes your organization's private definitions of features, deploys, customers, services on top - the enterprise graph
- 04Any agent issues a structured GraphQL query (or an MCP server turns plain intent into a structured query)
- 05Uvi enforces RBAC at retrieval time, on every node - data never leaves your environment
Result: A queryable model of how your company works. Structured answers, not document lists.
Glean optimizes the centralized-index model. Uvi introduces a new primitive: a knowledge graph of your organization itself, joined across systems through definitions a Forward Deployed Agent encodes with your team.
When to Use Glean vs. When to Use Uvi
Glean is a good fit when:
- The answer lives inside a single document somewhere in the company
- The primary user is a human knowledge worker (HR, IT, sales, support)
- Workloads are horizontal SaaS search - find a policy, a playbook, a doc
- Multi-tenant SaaS or single-tenant private cloud is acceptable
- You are not building autonomous agents that need to take action
Uvi is essential when:
- AI agents need to act on questions that span many systems
- The answer requires your organization's private definitions (features, deploys, services, customers)
- Context lives in code, tickets, PRs, Slack, and internal systems - not just SaaS docs
- Security is non-negotiable: full on-prem or air-gapped, zero data egress, RBAC at retrieval on every node
- You need a structured answer queryable in GraphQL or through an MCP server, not a list of links
Real-World Example: “Which features shipped behind feature flags last week?”
What an enterprise search platform returns
- Documents matching "feature flag" / "deploy" / "feature" from the last 7 days
- Possibly a generated summary of those documents
- No actual list of features - because no document contains that list
Agent output: A pile of links that probably do not contain the answer. The agent now has to read each one and guess.
What Uvi returns
- An exact list of features - joined from tickets, PRs, branch names, flag-key string matches, and the internal flag service
- For each: name, owning team, flag key, flag state, the PRs that landed it
- All resolved through the organization’s own definition of what a "feature" is, encoded by Uvi’s Forward Deployed Agent
- Returned as a structured GraphQL response
Agent output: The answer. Not a search result.
Frequently Asked Questions
Is Uvi a Glean replacement?
For knowledge-worker SaaS search (HR policy, sales playbook, IT runbook), Glean is purpose-built and fits well. For AI agents that need to take precise action across engineering and operational systems, Uvi is the right primitive - Glean was not designed for that workload. Many enterprises will run both.
Glean has a generated answer too - isn't that the same?
Glean's generated answer is a summary over the top-K retrieved documents - it is still ultimately a search result. Uvi's answer is the output of a structured GraphQL query against a model of your company. When the question requires joining many systems through your team's private definitions ("which features shipped behind flags last week"), search-plus-summary cannot reach the answer.
Why can't enterprise search answer questions about my own company's processes?
Most enterprise concepts have no document that defines them. A "feature" might be a Jira task that isn't a bugfix, linked to a feature flag in an internal flag service, joined to code only through branch-naming conventions. None of these links is written down. Search can return documents that mention the words; it cannot follow links that exist only as engineering habits. Uvi's enterprise graph encodes those habits into a queryable model.
Can Uvi be deployed on-premise?
Yes. Uvi runs fully on-premise or in your VPC, including air-gapped environments. Data never leaves your environment, and document-level RBAC is enforced natively on every node of the graph at retrieval time - not post-hoc.
How does Uvi handle the relationships Glean doesn't?
Uvi indexes the entire enterprise dataset, links documents across tools by explicit and implicit references (ticket ↔ PR ↔ code ↔ Slack), resolves entities so one person maps to a single identity everywhere, and exposes the result as a GraphQL-queryable graph. A Forward Deployed Agent - an ETL and data-science specialist - sits with your team to encode your organization's private definitions on top of that graph.
Make your company queryable
Book a technical demo to see how Uvi models your specific systems and definitions, and how it compares to enterprise search for AI-agent workloads.