Glean vs Company Brain: Enterprise Search Retrieves Documents, a Company Brain Remembers How You Work

Glean is the breakout story of enterprise AI search. The company crossed roughly 300 million US dollars in annual recurring revenue by May 2026, up from 208 million at the end of 2025, and carries a 7.2 billion US dollar valuation¹. Its pitch is simple and genuinely useful: point one AI search box at all 100-plus systems where your work lives, and stop hunting for the file you know exists somewhere.

That pitch answers a real and expensive problem. Depending on the study, knowledge workers spend between 20 and 30 percent of the workweek just looking for information¹⁵. If a tool can cut that, it pays for itself. So why do so many companies buy enterprise search, watch adoption plateau, and still feel like the important knowledge keeps walking out the door?

Because search and memory are not the same thing. Enterprise search retrieves documents. It finds and ranks what already exists. A Company Brain remembers: it holds the tacit knowledge, the decisions, the corrections, and the process context that no document ever captured, keeps it after people leave, and lets AI employees act on it. This is an honest comparison of Glean and the 2026 search field against that Company Brain idea. It is not a scorecard where one product wins every row. It is a guide to which problem you are actually trying to solve.

Enterprise Search Found Its Moment

For a decade, enterprise search was an unglamorous corner of IT. Large language models changed that overnight. Suddenly a search box could answer in full sentences, cite its sources, and feel like a colleague. Money and attention followed fast.

• Glean’s growth is real - the company reached roughly 300 million US dollars in ARR by May 2026, up from 208 million at the end of 2025, around 89 percent year-on-year growth, on a 7.2 billion US dollar valuation from its June 2025 Series F¹.
• The problem it targets is expensive - IDC-style studies put information retrieval at about 2.5 hours per day per knowledge worker, and McKinsey research puts search and gathering at roughly a fifth of the workweek¹⁵.
• The category is crowded - Glean, Microsoft 365 Copilot, Google Gemini Enterprise, Amazon Q Business, Guru, Coveo, Sinequa and open-source Onyx all now market some form of AI-powered enterprise search⁴.
• The buyer is under pressure - boards want an AI story, and a single search box across every system is the easiest one to approve and demo.
• Adoption is harder than the demo - Gartner data cited across the market found only 6 percent of organisations that piloted Microsoft 365 Copilot moved to a larger-scale deployment, and roughly 15 million of 450 million M365 subscribers had bought full Copilot licences by early 2026, a 3.3 percent conversion rate⁵.

So the momentum is genuine, and so is the underlying pain. The question is not whether enterprise search is useful. It is whether retrieval alone is the thing your company is missing.

Retrieval Is Not Memory

The clearest way to understand the difference is to watch what happens when you ask a hard question. Enterprise search returns the three most relevant documents. A colleague who has been at the company ten years tells you why the obvious answer is wrong, which customer is the exception, and who to call. Only one of those is retrieval.

What each one actually does

• Search finds, memory understands - retrieval ranks existing files by relevance; memory holds the reasoning, context, and corrections that were never written into a file.
• Search is a snapshot, memory accumulates - an index reflects the last sync; a Company Brain gets sharper every time someone corrects it or a decision is made.
• Search answers questions, an AI employee does the work - retrieval hands you documents; an AI employee working on top of a Company Brain takes the order from email to a confirmed record in the ERP.
• Search forgets people, memory keeps them - when an expert leaves, their files stay indexed but their judgement is gone unless something captured it.
• Search is generic, memory is yours - every company’s real process is a web of exceptions; retrieval treats all documents the same, memory encodes how you specifically operate.

This is not a criticism of search. It is a description of its boundary. The rest of this article maps exactly where that boundary sits, tool by tool, and what to do about the part beyond it.

What Enterprise Search Does Well

An honest comparison starts by giving search its due. There are jobs where a good enterprise search tool is exactly the right buy, and pretending otherwise would be dishonest and unhelpful.

• Ad-hoc discovery across many systems - finding the one deck, contract, or thread you half-remember across dozens of apps is precisely what retrieval is built for.
• Permission-aware answers - modern search respects access boundaries at query time, so people only see what they are allowed to see.
• Fast time-to-first-value - connect the systems, index the content, and users get useful answers in days, not months.
• Breadth over depth - Glean’s dual-graph architecture and 100-plus connectors give genuinely wide coverage across a heterogeneous SaaS estate².
• A natural-language front door - non-technical staff can ask a question in plain words instead of learning each system’s search syntax.
• Reducing obvious waste - if your people really do lose a fifth of the week to searching, even a partial fix is a real return¹⁵.

If everything you need already exists as a well-written document and your problem is simply finding it, search is the answer and you can stop reading. Most companies are not in that position, which is why the field is worth examining closely.

The 2026 Field: Glean and Its Rivals

Here is the honest lay of the land in 2026. Each of these is a real, current product with genuine strengths. The point is not to crown a winner but to show what each is built for and where its edge stops.

Glean

• What it is - the category leader in cross-vendor enterprise search, now positioning itself as a Work AI platform with a permission-aware knowledge graph, hybrid search, and agent tooling².
• Strength - breadth. It spreads widest across every tool regardless of who makes it, with 100-plus connectors and a dual enterprise-plus-personal graph².
• Actions - Glean reported over 270 million assistant actions in 2025 and offers 100-plus native actions, though multi-step business workflows remain a stretch for a retrieval-first product⁷.
• Pricing - no public list. Buyers report a base near 45 to 50 US dollars per user per month plus a consumption or AI add-on, a roughly 100-seat minimum, and an annual floor around 60,000 US dollars³.
• Watch-out - the pricing opacity and enterprise minimum put it out of reach for many mid-sized companies.

Microsoft 365 Copilot

• What it is - AI assistance embedded inside the Microsoft 365 apps your team already opens every day.
• Strength - depth inside Microsoft. It goes deepest into the Microsoft stack and rides on tenancy you already own⁵.
• Limit - scope. Copilot cannot search Salesforce, Confluence, Slack, custom databases, or anything outside Microsoft, so it is not a true cross-system search tool²⁰.
• Pricing - a 30 US dollar per user per month add-on on top of an M365 licence, with a newer M365 E7 bundle around 99 US dollars per user per month⁴.
• Watch-out - low conversion so far, with only about 3.3 percent of M365 subscribers on full Copilot licences by early 2026⁵.

The rest of the field

• Google Gemini Enterprise - launched October 2025, bundling enterprise search, a multimodal assistant, pre-built agents, and a no-code builder from around 21 US dollars per seat per month plus consumption⁶.
• Amazon Q Business - the transparent-pricing option at 20 US dollars per user per month Pro and 3 US dollars Lite, strongest in AWS-first environments¹⁹.
• Guru - a verified-answer approach where AI only answers from content human experts approved, starting near 25 US dollars per seat with a live Slack Model Context Protocol integration added in March 2026⁷.
• Coveo - relevance-focused search with 55-plus connectors, strong in commerce and sales enablement.
• Sinequa - the specialist for regulated industries with a hard-to-match security model and multilingual depth⁸.
• Onyx (formerly Danswer) - the leading open-source, self-hostable alternative with configurable models⁴.

Where Every Search Tool Hits the Same Wall

Because they share an architecture, the leading search tools share the same three structural limits. These are not bugs one vendor will patch. They are properties of building on retrieval, and they show up wherever the work depends on knowing how the company actually operates.

Wall 1: The index goes stale

• Snapshot, not live - most enterprise search runs on a pre-built index that only reflects the last sync, so fast-changing content drifts out of date¹⁶.
• Data freshness is the production gap - the difference between a proof-of-concept and a real deployment is often exactly how quickly documents and permissions re-sync¹⁶.
• Late-binding permission risk - when access is revoked but the index lags, a tool can still surface content a user should no longer see¹⁷.
• The 2026 workaround - vendors like Guru added live Model Context Protocol connections to query current data instead of a snapshot, an admission that the index model has limits⁷.

Wall 2: No memory of how you work

• It cannot retrieve what was never written - with roughly 80 percent of knowledge tacit, the reasoning behind decisions is invisible to an index¹¹.
• Corrections do not stick - tell a search tool it got the exception wrong and it re-indexes the same document tomorrow; nothing was learned.
• Turnover erases context - 48 percent of companies lose institutional knowledge with each departure, and search keeps the files while the judgement leaves¹².
• Governed context beats raw retrieval - analysts now argue the durable advantage is governed, structured context, not a bigger crawl of documents¹⁸.

Wall 3: It retrieves, it does not act

• The last mile is execution - finding the invoice is not paying it; finding the order email is not confirming it in the ERP.
• Assistants are not employees - assistant actions help a person work faster, but multi-step work across CRM, ERP and email is a different category⁷.
• Static tools stall - MIT found the pilots that failed did so because tools did not learn from or adapt to workflows, the exact failure mode of retrieval-only systems¹⁰.
• Partners outperform - the same MIT research found vendor-partner deployments succeeded about 67 percent of the time versus 33 percent for in-house builds⁹.

Nine Places the Search-Memory Gap Shows Up

The gap between retrieval and memory is abstract until you watch it in a real department. Here are nine concrete moments where enterprise search does its job perfectly and still leaves the outcome undone, because the missing piece was never a document.

1. Sales handover - search finds the account’s old emails and quotes, but it cannot tell the new rep that this customer never moves on unit price and only negotiates payment terms. That pattern lived in the departed account executive’s head.
2. Customer service goodwill - search surfaces the warranty policy PDF, but it cannot apply the unwritten goodwill threshold the service lead used for long-standing customers to keep them loyal.
3. Finance month-end - search retrieves last quarter’s close checklist, but it cannot run the accruals the controller did from memory or flag the cost centre that always posts late.
4. Procurement timing - search finds the supplier contract, but it cannot recall that this vendor’s lead times slip every December, so the reorder has to go out weeks early.
5. HR onboarding - search opens the onboarding wiki, but it cannot transfer the tacit map of who actually approves what, which a leaving manager carried in their head.
6. Production line - search pulls up the machine manual, but it cannot remember the operator’s specific fix for the recurring jam on line three that never made it into any document.
7. Quality control - search finds the audit template, but it cannot recall which supplier certificates of analysis are historically unreliable and need a second check.
8. IT service desk - search returns the runbook, but it cannot remember the undocumented workaround for the legacy VPN that only one admin ever knew.
9. Legal and contracts - search locates the master agreement, but it cannot recall the fallback clause the general counsel always accepts and the one they never do.

The Company Brain Alternative

A Company Brain starts from a different premise. Instead of indexing documents so people can find them, it builds a living memory of how the company works, keeps it current through daily use, and puts AI employees on top of it to do the routine work. Retrieval is one input, not the whole product.

What a Company Brain holds

• People-knowledge - the exceptions, the reasons, and the who-to-ask that experts carry in their heads and never document.
• Process context - how an order, a claim, or an approval actually moves through your business, including the workarounds nobody wrote down.
• Corrections over time - every time someone fixes an answer, the memory improves, so the same mistake is not repeated.
• Connections to real systems - it plugs into the tools you already run, from email and Teams to SharePoint, CRM and ERP, rather than sitting beside them.
• A model-agnostic core - the memory is the durable asset; the underlying model can change without losing what the company has learned.

How it changes the outcome

1. Knowledge survives turnover - when the person who knew the goodwill rules or the credit exceptions leaves, the reasoning is already in the Company Brain, not only in their head.
2. Work gets done, not just found - AI employees take routine tasks end to end, from an email order to a confirmed record in the ERP, with humans handling exceptions.
3. The system improves daily - feedback and corrections compound, so accuracy climbs instead of plateauing at index quality.
4. Answers reflect live state - because it connects to the systems where work happens, it is not limited to a snapshot from the last crawl.
5. New joiners ramp faster - instead of re-learning the undocumented rules, they inherit them.

From Search to Memory: A Practical Path

Building a Company Brain is not a rip-and-replace project, and it does not mean throwing away a search tool that works. It is a sequence of small, provable steps that turn scattered knowledge into a durable asset. Here is the path we see work in practice.

1. Map where knowledge lives - list your top ten processes and mark each one as documented or in-someone’s-head. The in-someone’s-head rows are where memory pays off and search cannot reach.
2. Pick one turnover-exposed process - start where a single person is the point of failure, because that is where the risk and the payoff are highest.
3. Keep your search layer - leave Glean, Copilot or whatever you run in place for discovery. A Company Brain sits underneath it, it does not compete with it.
4. Capture the tacit layer - sit with the expert and encode the exceptions, thresholds, and decisions into the Company Brain, so the reasoning stops being a single point of failure.
5. Connect the systems of record - wire in email, Teams, SharePoint, CRM and ERP so an AI employee can act on the knowledge, not just recite it.
6. Put an AI employee on the workflow - let it run the routine work end to end, with humans handling the exceptions it flags.
7. Close the feedback loop - every correction your team makes in the flow of work updates the memory, so accuracy compounds instead of resetting.
8. Measure against a baseline - track hours saved, ramp time for new joiners, and error rate, then expand to the next process only once the first one proves out.

Done this way, search and memory stop being an either-or choice. The search box keeps finding files, and the Company Brain quietly turns the knowledge behind those files into work that gets done.

Glean vs Company Brain, Head to Head

With both sides on the table, here is the direct comparison. Read it as a map of which tool fits which job, not as a leaderboard. Glean wins the discovery rows on purpose; a Company Brain wins the memory and action rows on purpose.

Most mid-sized companies discover their real pain is on the right-hand side. The files are findable enough; what hurts is the expert who is the only one who knows, and the routine work that still eats a skilled person’s day.

How Superkind Fits

Superkind builds a Company Brain and AI employees on top of it for SMEs and enterprises. The approach is process-first, not tool-first: the starting point is how your team actually works, not a generic search box you have to adopt. Superkind is one honest option here, and it is not the right buy for every job on this page.

• Company Brain as the core - a living memory of your people-knowledge, processes and corrections, not just an index of documents.
• AI employees that act - they take routine work end to end across the systems you already run, with humans on the exceptions.
• Sits on your existing stack - connects to email, Teams, SharePoint, CRM and ERP without rip-and-replace or a new platform to learn.
• Learns from daily feedback - your team corrects it in the flow of work, and the Company Brain gets sharper each week.
• Survives turnover by design - the reasoning and exceptions stay in the Company Brain when the person who knew them moves on.
• Model-agnostic memory - the durable asset is your knowledge, so a change of underlying model does not reset what the company has learned.
• Scoped per use case - start with one high-value workflow and clear, measurable outcomes rather than a company-wide seat licence.
• Built for the Mittelstand - data stays inside your infrastructure with encrypted connections, aligned to GDPR expectations.

Which One Should You Buy?

Start from the job, not the brand. This framework and checklist help you match your real pain to the right category before you sit through a single sales demo.

For a wider look at the search field itself, our honest 2026 buyer comparison of AI knowledge management and enterprise search tools goes tool by tool. This article is about the layer underneath that stack.

Frequently Asked Questions

Related Reading

• The Best AI Knowledge Management and Enterprise Search Tools: An Honest 2026 Buyer Comparison
• Enterprise AI Agent Platforms 2026: Copilot Studio vs Agentforce vs Vertex vs watsonx
• The Best AI Deep Research Tools in 2026 - and Why None of Them Knows Your Company
• When the Model Changes, Your Company Brain Shouldn’t
• Your Company’s Best Thinking Is Trapped in Personal ChatGPT Accounts

Henri Jung

Co-founder of Superkind, where he helps SMEs and enterprises deploy custom AI agents that actually fit how their teams work. Henri is passionate about closing the gap between what AI can do and the value it creates in real companies. Before Superkind, he spent years working with mid-sized businesses on digital transformation and saw first-hand how many AI projects fail because they start with technology instead of process. He believes the Mittelstand has everything it needs to lead in AI - it just needs the right approach.