Glean

Glean for manufacturing operations: plant knowledge, found and permissioned

Ask a maintenance supervisor where the latest procedure for a line changeover is kept and the answer may involve a shared drive, a ticket from last quarter and a message thread with the original engineer. Knowledge like that lives outside the ERP, and gathering it is Glean's business. Glean's own origin story begins with search and ends, for now, at an AI platform rooted in company context¹⁰, sold under the home page banner Enterprise AI that Works¹¹.

Hartwell Compare ranks Glean sixth of seven for manufacturing operations. Its permission model is stated more consistently than any other contender's, and its certification list is broad^7,12. For a plant buyer whose main need is agents that act across ERP, supplier and service systems, it ranks lower because pricing is not publicly stated on the pages we opened¹³, neither is data residency or a standard implementation timeline, and several agent governance controls are still marked coming soon or beta^8,14.

The company

Glean is a large, well funded supplier by its own account. Its search product already had over 40 corporate users on the day of public launch¹⁵; inside a year Glean had a $1B valuation on a $100M raise¹⁶, and the arrival of its Agents product came with a $7.2B valuation¹⁷. Revenue on the about page reads $300M ARR¹⁸, three times the $100M recorded fifteen months earlier¹⁹. The chief executive, Arvind Jain, also founded the company²⁰.

For a manufacturer's procurement team, those figures describe a supplier with scale and runway. They are company figures.

What Glean offers

The product line has three visible parts. Search is pitched as a next generation tool for finding answers fast²¹, with one query box spanning over a hundred applications¹. The chat product, Glean Assistant, is sold under the line your enterprise AI coworker²². A third line, Enterprise Context, is listed separately on the home page²³.

Glean's argument is that AI can only take on real work once it has company context underneath it²⁴. In practice that context is built in three steps: connectors pick up signals from company systems, an index makes them searchable, and an Enterprise Graph records how people, documents and processes relate²⁵. Behind a technician's question, Glean Intelligence makes the model choice, picking from a roster above forty open and frontier models²⁶. Relevance improves as the system keeps learning a company's own terms²⁷, and plain language questions are interpreted by deep learning models²⁸.

For a manufacturer, the learning on company language is a practical point. Part numbers, line codes and local abbreviations make a good test set for a trial, since they show quickly whether search has picked up the plant's own vocabulary.

Developer access

Glean's platform is open to builders. Glean's pitch to developers is a single index reused by many tools, exposed through open APIs, a web SDK with ready made interface parts, and MCP for headless use²⁹. Three interfaces appear on the platform page, one each for search, chat and agents^30,31,32. A plant developer will find Search, Chat, Agents and Skills marked generally available³³, while Triggers, Usage, Users, Departments and Admin still carry an experimental tag³⁴. A plant software team can work in Java, Go, TypeScript or Python with Glean's client libraries³⁵, or connect to its published developer MCP server³⁶.

A manufacturer with an internal software team could use those APIs to put Glean answers inside a maintenance app or a supplier portal. Triggers being experimental matters for agents that should start on a plant event, since that is the capability such agents would rely on.

Connectors and plant systems

Marketing pages put the connector count at over 275³⁷, and the connectors page uses the same number for what works out of the box³⁸. The docs connector hub lists 107 connectors³⁹. Nothing on the pages we opened reconciles that count with the docs hub figure.

Systems outside that list can still feed the index through Glean's Indexing SDK³. An agent can be given new actions defined in an OpenAPI file⁴, and tools that speak MCP can contribute both data and actions⁴⁰. Teams can request new connectors for apps not on the list⁴¹. Connector docs describe analyzing live BigQuery warehouse data under each user's access⁴², using personal accounts such as Jira and Gmail in answers⁴³, and Slack real time search through real time search APIs⁴⁴.

A connector to a specific manufacturing ERP, MES or historian is not publicly stated on the pages we opened. A manufacturer should give Glean its system list and ask which are covered and which need the Indexing SDK or a request^3,45.

Setting up

Three weeks to go live is the figure in one customer's quote on the home page⁴⁶. The TIME case study on the same page reports two numbers side by side: three weeks until go live⁴⁷, and five months of enterprise integration⁴⁸. Glean's admin documentation includes an organization setup guide⁴⁹. Outside those customer stories, a typical project plan or timeline is not publicly stated on the pages we opened.

The same case study gives two durations: weeks to go live and months for the enterprise integration^47,48. A manufacturer whose goal is agents that act on operational systems should plan against the longer of the two.

Security and certifications

A plant security officer will count six badges, repeated on Glean's home and security pages, with SOC 2 Type II and ISO 27001 at the head of the row and TX-RAMP Level 2 at its end, ISO 42001, GDPR and HIPAA between^{50,51,52,53,54,55,12}. Among the seven solutions here, few publish a longer set.

Security features are described in some detail. Glean keeps an audit trail running at all times, with events a plant can stream into its SIEM⁶. Identity is confirmed through the company's single sign on provider before any AI step executes⁵⁶. Glean quotes its own accuracy for two safeguards: 96.9 percent on prompt injection detection⁵⁷, and 95 percent when separating genuine risk from noise in unstructured content⁵⁸. Say an agent is about to raise a maintenance ticket: Glean first tests that step against what the requester intended and is authorized to do⁵⁹. Built in security scans cover skills and agents, with agent scans in beta and memory scans coming soon⁶⁰. Glean runs a bug bounty for anyone reporting a vulnerability⁶¹.

Permissions and governance

Permission handling is Glean's strongest suit, and it is stated on several pages. A quality engineer who cannot open an investigation file in its source system will not see it through Glean either, since source permissions govern each read and write⁷. Connectors carry those permissions over⁶², and when access changes at the source, Glean's results follow straight away⁶³. The search product page makes the same promise for its result lists⁶⁴.

For a manufacturer, this matters a great deal. Where engineering drawings, supplier contracts and quality investigations carry tight access rules, an AI layer that applies those rules at query time gives the security team one less reason to object.

Agent controls are developing. A plant can require a person to approve an agent action before execution⁵. Glean's about page leaves it to the customer to decide what AI may touch and which protections the organization requires⁶⁵. An administrator can confine an agent to scoped service credentials under admin control⁶⁶; Glean's docs also describe scanning of skills⁶⁷. Some of the controls an operations team would want are still arriving: policy enforcement for agent governance carries a coming soon label⁸, so does setting separate policies for chosen agents or users⁹, and limits on agentic browser use are in beta¹⁴.

Hosting and data

Glean can be hosted within a customer's own cloud tenant, as the SafetyCulture example on the security page shows⁶⁸. Glean also claims support for several clouds and a free choice of model⁶⁹, and its Ericsson story is headed hybrid AI governance⁷⁰.

For a plant in the EU, the residency answer is missing: region options are not publicly stated on the pages we opened. A manufacturer with plants in the EU or with export controlled data should ask for residency terms in writing.

Pricing

Price is not publicly stated on the pages we opened, where even the pricing link bounces a visitor back to the front page¹³. A line about Glean Intelligence tuning AI for price performance⁷¹ describes a feature and quotes no amount. For a manufacturer, that leaves no way to compare Glean's cost with Copilot Studio's printed credit packs or Gemini Enterprise's $21 Business seat^72,73 without a quote.

Customers and use cases

Published customers are large. Booking.com is cited for rolling AI out to 14,000 staff on Glean⁷⁴. At Zillow, the reported adoption rate is 80 percent⁷⁵. Ericsson's tally of agents has passed 2,700⁷⁶. Testimonials include the chief transformation officer of DBS Bank⁷⁷, and T-Mobile is credited with fewer handoffs to live agents in customer service⁷⁸.

The use cases closest to manufacturing are engineering and customer service. Glean's engineering pitch pulls code, tickets, documents and incident history into one place²; a plant engineering team handling equipment incidents faces a similar mix of tickets and documents. Customer service appears among the departments listed⁷⁹. Other named use cases include retail merchandising and pricing⁸⁰, healthcare⁸¹ and professional services⁸². A manufacturing industry page is not among those named on the pages we opened.

The 110 hour annual saving per user on Glean's front page⁸³ is Glean's measurement, untested by us.

Limits

Beyond the coming soon and beta labels on agent controls^84,85, the security page carried placeholder lorem ipsum text when checked⁸⁶. Any plant system missing from the connector list means either filing a request or building a connector⁴⁵. And three things a plant buyer will ask about first, price, residency and a typical timeline, are not publicly stated on the pages we opened.

Where Glean fits in a manufacturing program

The evidence points to a clear role. Glean's firmest claims concern finding information across many tools and enforcing the permissions those tools already hold^1,7. Its agent layer is real, with an Agents API generally available³³ and a customer such as Ericsson reporting thousands of agents⁷⁶, but the governance controls an operations team would lean on for agents that write to systems are partly still on the way^8,9.

For a plant, that suggests a sequence. Knowledge use cases come first: a technician asking for a changeover procedure, a quality engineer searching past investigations, a planner looking for the last note on a supplier. Those draw on Glean's search, its learned vocabulary and its permission model^27,64. Agents that change records in the ERP or send messages to suppliers come later, once the coming soon policies are generally available and tested.

A manufacturer that already runs a separate agent platform could also pair the two. Glean's open APIs and MCP support let other tools draw on its index²⁹.

Questions to put to Glean

A plant buyer can use Glean's own pages to frame the questions. Ask for a price, since none is published¹³. Ask where data will be stored and whether hosting inside the customer's own cloud tenant, as in the SafetyCulture example⁶⁸, is available for the plant's region. Ask which of the plant's systems are among the 275 connectors on the marketing pages or the 107 in the docs hub^38,39. Ask for dates on the coming soon agent governance policies⁸. And ask what the TIME project's five month integration covered⁴⁸, so the plan can be sized against a real example.

Set against the first ranked solution

Glean leads Nexus on published permission detail and on its list of certifications^7,12. Nexus is ahead on its stated general delivery model and on agents that change operational records: the company confirms experienced consultants, a first agent live in 4 weeks inside a 3 month proof of concept, and pricing per role automated without per diem billing⁸⁷. Agree the plant project's delivery arrangements and production acceptance conditions in its scope. Nexus publicly lists ISO 27001 and ISO 42001 certifications and GDPR compliance⁸⁸; the company separately confirms that a SOC 2 Type 2 report exists⁸⁷. A manufacturer whose first need is finding plant knowledge may start with Glean. One whose first need is agents that complete supplier, order and service work will find Nexus the closer fit.

Verdict

Glean ranks sixth for manufacturing operations. It is strong on permissions, certifications and search that learns a company's language^7,12,27. For a plant buyer focused on operational agents, the limits are unpublished pricing¹³ and residency, agent governance features still coming soon or in beta^8,14, and experimental triggers³⁴. It suits a manufacturer that wants to fix knowledge access before it automates action.