Glean is workplace search that grew a work assistant on top. It finds files across Drive, Slack, Confluence, and the rest of the workplace — and it respects who is allowed to see them. Nimbus is the place you then run the job those files imply: update the customer record, file the next step, get a person to sign.

Glean’s centre of gravity is find the right object, for the right person, at the right time. Nimbus’s is a signed-off outcome. Both products will say “knowledge” and “agents.” They are not the same purchase. Search that does not leak is a hard, years-long problem. A release on a live system is a different hard problem. Many enterprises have both. They should not pretend one vendor covers both because both say “knowledge graph.”

Glean’s product connects to Google Workspace, Microsoft 365, Slack, Salesforce, Confluence, Jira, and many more — publicly, 100+ workplace apps — then builds an index that respects the same permissions as the source system. Under the hood that is a mix of classic keyword search and meaning-based search, plus a map of people, documents, and activity. Glean has added assistants and agents so the search layer can also draft and automate. That architecture is why Glean wins large-enterprise search bake-offs. Identity, crawl, and permission fidelity are the hard problems, and Glean has spent years on them.

Words you’ll hear

Workplace search. An index across the apps the company already uses, so people stop hunting for files.
Permission mirroring. If a document is restricted in Drive, Glean should not surface it in a chat answer. That is the product.
Knowledge graph (Glean). A map of people, documents, and activity for retrieval. Not the same as Nimbus’s Lifecycle Graph.
Crawl. The programme of connecting apps, mapping identity, and keeping the index fresh. Why IT sponsors Glean. Why it takes time.
Workstream. In Nimbus, a shared workspace for one job — not a search result.
Write-back. Changing a live system. Search products add agents on top of the index. Nimbus treats the write as a first-class release.
Lifecycle Graph. A causal record of AI work: what ran, who approved it, what changed. Not an index of every file an employee ever touched.
Copilot. Microsoft’s assistant inside Office. Glean searches across many apps, including Microsoft. Neither is a governed execution layer.

Why the difference matters

If you have ever watched a naive chatbot answer from a restricted Drive folder, you understand why Glean exists. Permission mirroring is not a slogan. It is the product: if a document is restricted in Drive, Glean should not surface it in a chat answer. NIST SP 800-53 Rev. 5 Access Control (the AC family) is the control-catalogue reason: the system must enforce who may see what. Glean’s security page describes that enforcement as a product: permission mirroring, encryption, and compliance claims. Glean is search that does not leak. It is not an AI risk-management framework, and it does not, by itself, put a named signer on a customer-record write.

Glean’s primary object is a document, message, ticket, or person. Nimbus’s primary object is a job, an agent team, a release. Glean’s success metric is time-to-answer. Nimbus’s is time-to-signed-off outcome. Glean’s write path is secondary — agents on top of the index. Nimbus’s write path is first-class: read-only until you open it, then a person on the change.

Ask whether agents are a feature of search, or search is a feature of agents. Glean is the first. Nimbus is the second. Assistants on a permission-aware index are a reasonable next step for a search company. They still orbit findability. A workstream does not orbit a search result. It orbits a finish line, a budget, and a write policy.

The crawl is why IT sponsors Glean and why it takes time: connecting apps, mapping identity, keeping the index fresh, proving permission QA. That is the right model for a 20,000-person corpus. It is the wrong model if you needed a signed CRM update this quarter and were told to wait until 2019’s files had finished indexing. Nimbus assumes you can already find the policy, or that you will attach the systems this job needs. It does not wait for a company-wide crawl of every historical file.

The two “graphs” are the usual confusion. Glean’s knowledge graph is mostly an index of people and content for retrieval. Nimbus’s Lifecycle Graph is an operational ledger of work, agents, and releases. Collapsing the terms in a vendor meeting is how you buy the wrong one. You can run both graphs. You cannot substitute one for the other.

Role by role, the fork is practical. A CIO running a knowledge programme wants a universal search bar across 100+ apps, with permission mirroring as the non-negotiable. That is Glean. A Head of RevOps wants an agent to propose opportunity updates with a named signer — search will find the deck; it will not be the release. Security cares that Glean does not leak restricted files, and still cares who may change Salesforce. Knowledge workers want time-to-answer. Operators want time-to-signed-off outcome. Legal will not accept “the assistant found it” as the story of why a customer field moved.

A healthy split: Glean for findability across the sprawling workplace; Nimbus for execution on the jobs that mutate systems of record. Do not stretch Glean into an operating layer because it added agents. Do not stretch Nimbus into a crawl of every Confluence page because Perception can answer questions about the record.

When Glean is a better fit

Choose Glean when the corpus is huge, permissions are the product, and you need a universal search bar across 100+ apps before you invent agent teams. Choose Glean also if the executive sponsor is the CIO’s knowledge programme rather than a line-of-business operating model.

Deployment is a crawl programme: identity mapping, permission QA, often with Glean or partner engineers in the building. That is the right model for a 20,000-person corpus. Do not treat that implementation cost as a reason Glean is “worse.” It is the cost of doing permission-aware search well.

Do not choose Glean as a stealth agent operating layer. You will spend a year on crawl quality and still lack workstreams, specialist teams, and change control on writes.

Some organisations will run Glean for findability and Nimbus for execution. That is a coherent architecture if you do not pretend one graph is the other. Keep Glean as the place people find the deck. Put the job the deck implies — update the customer record, file the next step, get a person to sign — in a Nimbus workstream. Connectors in Nimbus are not a substitute for Glean-scale historical crawl. A Glean assistant is not a substitute for a quoted write.

How this shows up in Nimbus

Search exists inside Nimbus as a way to ask about the record, your playbooks, and the systems you attached. It is not a company-wide crawl of 2019.

The wiki is what the company asserts. Connectors are scoped per workstream. Agents do not get “search everything this user could theoretically open” as the default tool. They get the systems you attached, in the mode you allowed (usually read), until a human releases a write. Perception is ordinary language over that scoped world, not a second Glean.

You can set Nimbus up yourselves: a workspace, wiki, connectors, a first workstream. You do not wait for an index of every historical file to finish.

See Governance and the Lifecycle Graph.

Questions people actually ask

Does Glean require more implementation than Nimbus?

Usually yes. Permission-aware crawl at enterprise scale is a programme. Nimbus is self-service for most buyers: you are not waiting on vendor engineers sitting with your team for months to get collaboration, sign-off, and a causal record. That is not a claim that Glean is slow for no reason. Crawl and permission QA are the work.

Does Nimbus replace Glean?

Only if your Glean usage is a thin Q&A bot on a small corpus. It does not replace Glean as permission-aware enterprise search across a sprawling workplace. If you need the latter, keep a search product.

Both mention knowledge graphs. Are they the same?

No. Glean’s graph is mostly an index of people and content for retrieval. Nimbus’s Lifecycle Graph is an operational ledger of work, agents, and releases. Collapsing the terms in a vendor meeting is how you buy the wrong one.

Can Nimbus search Drive and Slack?

Connectors bring live systems into agent context and into questions you ask about the record. That is targeted operational retrieval, not a Glean-scale crawl of every historical file. If you need the latter, keep a search product.

Glean vs Copilot vs Nimbus?

Copilot is productivity inside Microsoft that respects Microsoft permissions. Glean is search across many apps (including Microsoft) that respects source permissions. Nimbus is governed execution. A Microsoft-first company may still need Glean if SharePoint search is not enough, and may still need Nimbus if Copilot Studio is not an operating layer. See Nimbus vs Microsoft Copilot.

Can we run Glean and Nimbus together?

Yes. That is the intended coexistence for organisations that have both a findability problem and an execution problem. Do not give Glean agents production write credentials because the index is permission-aware. Permission to see is not permission to change. Route mutations through Nimbus governance.

Who should own which product?

The CIO’s knowledge programme typically owns Glean: identity mapping, crawl, permission QA. Line operators own Nimbus workstreams because they own the systems of record those jobs touch. Security reviews both — leak prevention on the search side, write gates on the execution side.

If Glean has agents now, why add Nimbus?

Because agents on an index are still a feature of search. Time-to-answer is not time-to-signed-off outcome. If the job is to change a customer record with a person on the write, you need a workstream, a wiki clause, and a graph — not another way to find the deck.

What is enterprise RAG, What is a lifecycle graph, and Nimbus vs Microsoft Copilot.

Nimbus vs Glean: Finding the Deck vs Running the Job That Deck Implies