What Is Glean?
Glean is an enterprise AI search platform. The short version: it connects to every SaaS app your company uses — Slack, Google Drive, Jira, Confluence, Salesforce, GitHub, Zendesk, and 100+ more — and builds a unified search index that understands natural language questions. You ask "what is our Q3 marketing budget?" and it finds the answer across spreadsheets, Slack announcements, Confluence pages, and email threads, then synthesizes a response with citations.
I started deploying Glean for clients in early 2025. Before that, my consulting work was general IT and SaaS implementation — setting up Google Workspace, migrating companies to Slack, that kind of thing. Glean changed the economics of my business because it solves a problem that every company with 50+ employees has and almost none of them realize they have: their knowledge is scattered across 8-12 different apps and nobody can find anything.
Here is what I mean. Walk into any mid-size company and ask a manager "where is the latest version of the Q3 sales deck?" They will open Slack, scroll for 5 minutes, check Google Drive, find 4 different versions, Slack-message 3 people to ask which one is current, and 20 minutes later they have an answer they are 70% sure about. Glean answers the same question in 3 seconds with a link to the right file. That is the pitch, and it works because every manager has lived that frustration.
Who Glean Is Actually For
Three groups get the most value:
Mid-size companies (100-2,000 employees). These are the sweet spot. They have enough SaaS tools that search is genuinely broken — usually 8-15 different apps generating documents, messages, and tickets — but not so large that they have a dedicated internal search team. They are also big enough that the per-seat pricing math works out. A 300-person company spending $45,000/year on Glean recovers that in the first quarter just from reduced search time.
Knowledge-heavy industries. Law firms, consulting agencies, engineering teams, healthcare organizations. These are places where finding the right document quickly has direct revenue implications. A consultant who spends 15 minutes finding a past project deliverable instead of 2 minutes saves 13 minutes per search. At 10 searches a day, that is 2+ hours back — worth $200-$400/day at consulting rates. These clients see the ROI immediately and rarely push back on pricing.
Companies going through M&A or rapid growth. When two companies merge, you now have two of everything: two Google Drives, two Slack workspaces, two Jira instances, two Confluence wikis. Finding anything becomes a nightmare. Glean is the first tool I recommend during post-merger integration because it bridges the knowledge gap while the IT team spends 6-12 months consolidating systems.
The Monetization Model: How I Make Money With Glean
I do not work for Glean. I am an independent consultant who deploys Glean for clients and charges for it. Here is the model that has worked for me.
Phase 1: Discovery and Planning ($2,000-$5,000). Before touching any software, I spend 1-2 weeks auditing the client's SaaS stack. What apps do they use? Who uses them? What are the permission structures? Where do people currently go to find information and where do they get stuck? I interview 5-10 people across departments — a VP, a manager, an engineer, a salesperson, a new hire who joined in the last 3 months. The new hire interview is always the most revealing because they remember what was impossible to find during onboarding.
The deliverable is a 10-15 page implementation plan: which apps connect, in what order, permission mapping, estimated timeline, identified risks (custom apps, legacy systems, compliance concerns), and a prioritized list of "high value queries" that should work on day one. This document also serves as the internal business case the client's champion uses to get budget approval from their CFO.
Phase 2: Deployment ($5,000-$15,000). This is the hands-on work. I set up the Glean instance, connect the standard apps (this part is fast — about 2 hours for Google Workspace, Slack, Jira, and Salesforce), configure SSO and SCIM so user provisioning is automatic, and trigger the initial index. Then I spend 3-5 days on the harder stuff: custom connectors for any in-house tools, relevance tuning for the priority queries identified in Phase 1, and building the initial set of curated results for commonly searched terms like "PTO policy" or "expense report template."
The last day is a training session with the internal team — usually the IT lead and a department champion from each major group. I teach them how to tune relevance, add curated results, and read the analytics dashboard. The goal is that after I leave, they can run the system without calling me every week. (They still call, but less often.)
Phase 3: Ongoing Retainer ($500-$2,000/month). Most clients sign a monthly retainer after deployment. The work is light — 4-6 hours a month — but consistent. I review the search analytics to spot zero-result queries (things people searched for that returned nothing — a signal that content is missing or the connector is broken), add new apps as the company adopts tools (someone inevitably signs up for Notion or Linear and wants it connected), run quarterly relevance reviews to retire stale content and promote fresh documents, and send a one-page report to the executive sponsor showing usage stats and productivity estimates.
At $1,000/month average across 6 clients, that is $72,000 a year in retainer revenue with maybe 30 hours of work per month. The rest of my time goes to new deployments, which are one-time project fees of $5,000-$15,000 each. I do about 6-8 deployments a year.
Total annual revenue: $72,000 (retainers) + $60,000-$120,000 (deployments) = $132,000-$192,000 per year as a solo consultant. Costs: a laptop, occasional subcontractor help for custom connector development ($5,000-$10,000/year), and travel to client sites for kickoff meetings. No office, no employees, no inventory.
What Glean Does Well
The permission-aware search is the feature that closes deals. I have been in rooms where the CTO is excited, the department heads are nodding, and the general counsel is frowning with arms crossed. Then I demonstrate that an engineer searching for "compensation" sees zero results from HR's Drive folder because Glean inherits Google Drive's sharing permissions. The lawyer uncrosses their arms. The deal moves forward.
This is not a "nice to have" feature. It is the difference between "we will pilot this with the engineering team" and "we will deploy this company-wide." Without permission-aware search, Glean is a security liability. With it, it is compliant out of the box. If you are selling Glean implementations, learn to demo this feature in the first 5 minutes. It answers the objection before anyone voices it.
The AI Answers quality surprised me. I was skeptical — most "AI-powered" enterprise features in 2024-2025 were thin wrappers around GPT-3.5 that produced generic responses. Glean's Answers feature actually synthesizes across real documents with citations you can click. When a VP asks "what is our churn rate for enterprise customers last quarter?" and it pulls the exact number from the board deck in Drive, the CS team's quarterly report in Confluence, and the CFO's summary email in Gmail — and cites all three — the room goes quiet in a good way. That is the moment the client decides to buy.
The connector ecosystem is genuinely maintained. This matters more than it sounds. I have used "universal search" tools before where the Slack connector was built by one engineer in 2021, never updated, and breaks every time Slack changes their API. Glean has a dedicated integrations team that keeps connectors working. In 18 months of deployments, I have had exactly one connector break (Salesforce, during a major API version migration), and Glean had a fix within 48 hours.
Where Glean Falls Short
The pricing opacity is my biggest frustration as a consultant. I cannot give a client a simple answer to "how much will this cost?" I have to say "let me introduce you to their sales team" — which feels slimy and slows down deals. I have lost at least two clients who got impatient during the sales process and decided to "just use Slack search better" instead. If Glean published a transparent pricing page with per-seat tiers, my close rate would jump 20-30% overnight.
The index speed on large datasets tests client patience. One client had a Google Drive with 800,000+ documents accumulated over 12 years. The initial index took 4 days. For 4 days, the client's champion (who had sold this internally) had to tell executives "it is still indexing, check back tomorrow." By day 3, enthusiasm had noticeably cooled. Glean needs a way to prioritize critical content — index the executive team's Drive folders first, the marketing team's second, and the 2014 archive last — so that the tool is useful on day one while the full index finishes in the background.
The AI Answers feature cannot handle contradiction well. In any organization, documents disagree. The Q3 forecast spreadsheet says $2.1M in new ARR. The CEO's Slack message from yesterday says "we are tracking toward $2.4M." Glean sometimes picks the older, more formal document over the newer, more casual one. When a VP gets a wrong answer with confident citations, it erodes trust. I now train every client that AI Answers are a starting point, not a final answer — always click through to the source documents.
Custom connectors should not be as hard as they are. Glean's API is well-documented, but "well-documented" does not make building a connector for a homegrown PHP CRM from 2018 any less painful. The connector SDK assumes a modern REST API with OAuth. Legacy systems often use SOAP, session cookies, or worse — CSV exports to a shared folder. I subcontract a developer for most custom connector work, and the cost ($5,000-$15,000 per connector) sometimes kills the deal for smaller clients. A low-code connector builder — something like Zapier's interface for building API integrations — would solve this and expand the addressable market significantly.
Getting Started as a Glean Consultant
If you want to build a Glean consulting practice, here is what I would do if I were starting over:
First, deploy Glean for yourself. Set up a free trial on your own Google Workspace and Slack. Connect your apps, run the index, use it for 2 weeks. You need to experience the product as a user before you can sell it. Pay attention to what surprises you — what searches work better than expected, what searches fail, what the analytics dashboard actually shows. These become your demo talking points.
Second, find your first client through your network. Do not cold-call. Post on LinkedIn that you are exploring enterprise AI search implementations and looking for a beta client. Offer to do it at cost (or even free, with the agreement that they serve as a case study). Your first deployment will take 3x longer than you estimate and you will make every mistake in this article. Better to make those mistakes on a friendly client who knows it is a learning experience.
Third, document everything. After each deployment, write down what went wrong, what you would do differently, and what the client said that surprised you. Within 3 deployments, you will have a repeatable playbook. Within 6, you will have enough case study material to sell to strangers. The consulting business is not about being the smartest person in the room — it is about having seen the problem before and knowing exactly what step comes next.
The tools you will need beyond Glean: A project management tool (I use Notion), a contract template for SOWs and MSAs (worth paying a lawyer $500 to draft once), a time tracking tool for retainer billing, and a basic understanding of OAuth, SAML, and REST APIs. You do not need to be a developer, but you need to be able to have an intelligent conversation with one. If a client asks "can you build a connector for our internal tool?" you need to know enough to scope the work and hire the right person.
Bottom Line
Glean solves a real problem — enterprise knowledge is scattered and search is broken — and the monetization model for consultants is straightforward: plan the deployment, execute it, charge a retainer for ongoing management. The math works for solo consultants at 4-8 clients with $130,000-$190,000 annual revenue. It works even better for small agencies that can handle 15-20 clients with a team of 2-3 people.
The product is not perfect. Pricing opacity, slow indexing, hallucination on contradictory data, and custom connector complexity are real problems that will cost you deals and create support headaches. But the core value proposition — "find anything across all your apps in 3 seconds" — is strong enough that clients buy despite the rough edges.
If you are already doing IT consulting, SaaS implementation, or digital transformation work, adding Glean to your service portfolio is a natural extension. It is not a passive income play. It is a services business. But it is a services business where the product sells itself once you demo it, the retainer revenue is recurring, and the competitive moat is real — once a client has Glean running across 10+ apps with tuned relevance and trained users, switching costs are high enough that they will not churn over a price increase.
One piece of honest advice: start now, not in 6 months. Enterprise AI search is still a fragmented market in 2026. Glean, Sinequa, Coveo, and Elastic are competing, but no one has locked it up. The consultants who build expertise and case studies in the next 12-18 months will own the category when it consolidates. The people who wait for "the market to mature" will be competing on price against established players with 20+ case studies.