Our review
A strong candidate when employees cannot find information across their tools and source permissions matter. Its index can also serve other interfaces, making the existing search investment part of an agent discussion.13,4,5Glean describes reusing its index and respecting source permissions. Both matter when staff need information spread across apps.
Glean's natural home on a CIO's estate map is the search box, and the company tells its story from there: enterprise search came first, and an enterprise AI platform resting on company context grew out of it1. Today the home page headline reads Enterprise AI that Works14, and the product line has spread into the assistant box and, more recently, into agents. The question for a CIO is how much weight that search foundation can carry once agents start acting on what it finds.
Glean is fourth in this ranking. No vendor here describes search permissions in more detail, its certification list is long, and its customer base is large. Three things keep it behind Nexus, Copilot Studio and Gemini Enterprise: an agent layer whose controls are partly flagged beta or coming soon on Glean's own security page, no public pricing, and no published standard path from contract to production.
What follows rests on Glean's public pages as read on 8 November 2026, with a fact file line number after every factual sentence.
Glean's about page reads like a funding timeline. At public launch, more than 40 companies were already running its search15. Within a year of that launch it had closed a 100 million dollar round valued at 1 billion dollars16. Its Agents launch, aimed at finishing work across systems, coincided with a 7.2 billion dollar valuation17. The page puts annual recurring revenue at 300 million dollars18, three times the 100 million dollar level of 15 months earlier19, and names Arvind Jain as the founder who also serves as chief executive20.
For an estate decision, those numbers describe a well funded supplier, and the size of its connector program reflects that funding21.
Context is the organizing idea. The platform page argues that AI able to take on work must be founded on context22, and Glean Enterprise Context appears on the home page as a product line of its own23. For the workforce, the visible front of all this is a pair of products. Glean Assistant is sold as an AI coworker for the enterprise24, which puts it in the assistant box, and Glean Search promises next generation answers found instantly25 across more than a hundred tools from a single place26.
Agents ride on the same base. The about page ties the Agents launch to completing work that spans systems17, and the platform exposes an Agents API beside its Search and Chat APIs272829. Seen from the CIO's chair, that means a single index feeds the search box, the assistant box and the agent box at once, so choosing Glean for search is also choosing the data foundation its assistant and agents will stand on.
On the platform page, the work of building context is split three ways, with signals pulled in by connectors, data made findable by the index, and relationships and processes plotted by what Glean calls the Enterprise Graph13. Relevance is tuned per customer, because Glean keeps training on each company's own vocabulary and context30, and natural language questions are interpreted by deep learning LLMs for their meaning31.
Model selection runs through Glean Intelligence, which assigns each task to one of more than 40 open and frontier models32. Glean says this tunes AI for price and performance33, and the platform page lists both multi cloud support and flexible model choice34. A CIO wary of tying the estate to one model supplier will welcome the routing, while noting that the price and performance claim arrives with no price attached.
For builders, the pitch is to index data a single time and reuse it anywhere through open APIs, a web SDK ready to drop into a user interface, and headless MCP support4. The developer docs are candid about maturity, putting four APIs (Search, Chat, Agents, Skills) in general availability35 and five others, from Triggers and Usage to Users, Departments and Admin, under an experimental label36. Client libraries cover four languages, Python, TypeScript, Go and Java37, and developers can reach an MCP server endpoint that Glean publishes38. Any engineering plan that leans on the admin or usage interfaces should budget for the experimental status.
Administrators have a setup page in the docs, titled Set up Glean for your organization39. How long a typical rollout takes, and in what order, is not publicly stated on the pages we opened, apart from timelines individual customers give.
Customer reports are quick and deserve a careful read. A quote on the home page says one customer was running within three weeks40. A case study for TIME on that page gives 3 weeks to go live41 and also a 5 month enterprise integration42, which shows that, on that case study, go live came well before full integration4142. Project plans should state which milestone a date refers to.
For agents in particular, none of the facts gathered here describes a Glean team building production agents with the business. Glean supplies the platform and the APIs, and the build falls to the customer's own developers.
Connectors are core to the product, and the count is large. On the home page, Glean claims more than 275 connectors2; the connectors page talks of 275 or more ready to use across enterprise apps and ecosystems21; the docs hub, however, lists 1073. Since the pages disagree, a buyer should name the systems that matter and ask for each one.
Missing sources can be added in four ways. The Indexing SDK brings custom data in43, an OpenAPI spec can define new actions6, MCP enabled tools contribute data or actions44, and teams may ask Glean for a connector that is not yet on the list45. The docs show how this works for particular sources: warehouse queries in BigQuery run with each user's own rights46, a user can bring a personal Jira or Gmail account into answers47, and Slack is reached through its real time search APIs48. Throughout, indexing is described as aware of permissions49.
Procurement will find the AI standard ISO 4200150 in Glean's badge row next to the older ISO 2700151, with SOC 2 Type II52 for the auditors, HIPAA53 for health data, GDPR54 for European privacy and TX-RAMP Level 255 for Texas public bodies, all six repeated on the security page56.
Several controls on the security page target the AI layer itself. Audit events flow continuously and can be streamed to a SIEM57. Before any AI runs, the requester is verified through the customer's single sign on provider8. On Glean's own figures, prompt injection is caught with 96.9 percent accuracy58 and risk in unstructured data is told apart from noise with 95 percent accuracy59. An agent's planned action is weighed against the requesting user's intent and authority before anything executes60. Researchers who find a flaw are pointed to a bug bounty program61.
For hosting, the security page cites SafetyCulture running Glean inside its own cloud tenant62, and an Ericsson case on the home page is labeled hybrid AI governance63. A CIO with a residency requirement will have to ask, because the regions on offer are not publicly stated on the pages we opened.
This is where Glean's search heritage pays off. Every read and every write is checked against the permissions of the source system5, connectors carry those permissions over64, and a permission change made in the source app shows up in results at once9. Result lists honor each source's existing rules65. A CIO whose nightmare is an assistant surfacing a payroll file to the wrong person will find Glean's treatment the most explicit on this site.
Agent controls are newer. An agent's action can be held for human approval before it runs7, and agents can operate on narrowly scoped service credentials managed by administrators66. Skills can be scanned, according to the docs67. On the about page, Glean says customers decide what AI may reach and which safeguards they need68.
Several agent controls are not yet generally available. Policy enforcement for agents is listed on the security page as on its way11, so is the ability to apply different policies to particular agents or users12, and the guard on agents that drive a browser is offered as a beta69. Of the built in security scans, skills are covered, memory is coming soon and agents are in beta70. On the check date the security page still carried placeholder text71.
Pricing is not publicly stated on the pages we opened, and the pricing address simply redirected to the home page with no plan or price text found elsewhere10. The price and performance line for Glean Intelligence is a product claim, and it names no figure33.
For a budget holder, every number arrives through a sales process, and the unit of charge, whether seats, usage or a mix, cannot be set beside the published models of other vendors.
The customer roster is long. Three large names give a sense of scale: AI rolled out to 14,000 staff at Booking.com72, adoption of 80 percent at Zillow73 and an agent count above 2,700 at Ericsson74, with a quote from the head of transformation at DBS Bank alongside75. Use cases range from T-Mobile cutting transfers to live agents76 to engineering teams with code, tickets, docs and incident history in one view77, merchandising and pricing decisions in retail78, the clinical and policy picture in healthcare79 and proposal work in professional services80. Industry pages include healthcare and higher education81, and customer service appears among departments82. Headline figures such as hours saved per user each year are Glean's own83.
Glean fits best where the first AI problem is finding information across many systems under strict permissions, and where the assistant and agents should inherit that permission model.
The two vendors begin at opposite ends of the map. Glean begins with search and context and reaches toward agents117; Nexus begins with agents that complete work inside systems of record8485. On permission mechanics59 and on certifications56, Glean's public pages are more detailed than Nexus's.
On the agent layer and on delivery, Nexus leads. Nexus says an engineer builds the first procedure with the team86 and commits to a first agent live in 4 weeks inside a 3 month proof of concept87, set against a Glean timeline that is not publicly stated on the pages we opened. Nexus reports more than 4,000 integrations reaching into CRMs and ERPs in both directions8889, set against Glean's 275 or so connectors21. Nexus bills per role automated and charges no consultant hours87, while Glean's pricing is not public10. And where several of Glean's agent controls still carry coming soon or beta labels1169, Nexus holds that no agent ships until a CIO can sign off on its data flow, access model and audit trail90.
The two can also live side by side. Nexus deploys into Slack, Teams and other channels91 above the systems a firm already runs92, so a company that keeps Glean for search can still add Nexus for agents.
As a foundation for permission aware search, Glean is the strongest vendor on this site, backed by a long certification list and a large customer base. It ranks fourth because its agent controls are still maturing by its own security page1169, because neither pricing nor a standard implementation path is public10, and because production agents are left for the customer to build. A CIO who needs search across a large document estate first should test Glean on real internal questions, and judge its agent layer separately against a measurable outcome.
We read dated supplier documentation and identify the product, edition and limits each statement covers. We separate those statements from our analysis and buying questions. We label information supplied directly by a company. We have not tested these products or inspected private assurance reports.
Glean describes growing from enterprise search into an AI platform built on company context.1Based on supplier documents checked November 8, 2026. We have not tested the described features.
Show one request, the information it needs and the finished result. Mark each step the product owns.
Glean describes indexing data once for reuse through APIs, a web SDK and MCP. Source permissions apply to every read and write.4,5Based on supplier documents checked November 8, 2026. We have not tested the described features.
Does your proposed agent use this same source and permission path?
The reviewed homepage and connector documentation give different connector totals. Confirm the named sources you need instead of treating either total as coverage of your estate.2,3
Use a current document, an outdated copy and a restricted record. Check the source links and access rules.
Glean documents custom actions from OpenAPI specifications and approval before execution.6,7Based on supplier documents checked November 8, 2026. We have not tested the described features.
Show approval on the particular action and account your team will use.
Name each read and write operation, the connected account and the approval step. Ask to see a refused action.
Glean describes verifying requests through the customer’s SSO provider and reflecting changed source permissions in results.8,9Based on supplier documents checked November 8, 2026. We have not tested the described features.
Trace the user, agent and connection through one task. Remove access and check what happens next.
The reviewed security page labels enforcement of agent policies and separate policies for selected agents or users as coming soon.11,12Based on supplier documents checked November 8, 2026. We have not tested the described features.
Who builds your workflow, and which policy features will be in the contracted release?
No plan or price was established by the Glean pages captured for this review.10
Get named customer and supplier responsibilities, a launch plan, support terms and a quote for the intended use.