1. Headline & intro
Every large software vendor now has an AI assistant, but very few have a convincing answer to a harder question: who actually understands your company well enough to trust with real work? That gap is where Glean is trying to build a business. Instead of competing for the glossy chat interface, the company wants to be the neutral intelligence layer that connects large language models (LLMs) to messy enterprise reality — permissions, processes, politics and all. In this piece we’ll look at what Glean is really selling, why it matters in the current AI land grab, and whether a standalone middle layer can survive the ambitions of Microsoft and Google.
2. The news in brief
According to TechCrunch’s Rebecca Bellan, Glean has evolved from an “enterprise Google” search tool into a broader AI infrastructure layer for large organisations. Founded seven years ago, the company began by indexing data across tools like Slack, Jira, Google Drive and Salesforce to provide unified search. Today, it uses that same mapping of people, documents and permissions to power AI assistants and agents.
Glean’s platform sits between customer data and a mix of LLMs such as ChatGPT, Gemini and Claude, as well as open‑source models. It offers three main pieces: access to multiple models, deep connectors into SaaS tools, and a governance layer that respects internal permissions while reducing hallucinations through citation and verification. In June 2025, Glean raised a $150 million Series F round at a $7.2 billion valuation, TechCrunch reports. The company positions itself as a neutral partner rather than a competitor to the big AI labs or productivity suites.
3. Why this matters
The first wave of enterprise AI has been dominated by shiny assistants: Copilot in Microsoft 365, Gemini in Google Workspace, and a forest of chatbots embedded into every SaaS product. Most of them demo well but hit the same wall in production: generic models with shallow access to company context. Glean is explicitly going after that wall.
If it works, Glean gives CIOs three things they care about: less vendor lock‑in, safer data use, and a path from “pilot” to “actually deployed across 50,000 employees.” By abstracting over multiple LLMs, Glean lets enterprises switch models as capabilities, pricing or regulation change. That is not a nice‑to‑have; it is risk management in a market where model quality and legal exposure are both moving targets.
The governance piece may be even more important than the multi‑model story. Enterprises already have scars from search and collaboration tools that leaked documents across teams. An AI layer that is deeply permissions‑aware and can show its work with line‑by‑line citations is much easier to sell to security, legal and compliance teams than a black‑box chatbot.
The losers, if Glean’s vision plays out, are the single‑suite vendors who want Copilot or Gemini to be the only brain enterprises use. If the intelligence layer is unbundled and sits outside the productivity suites, Microsoft and Google’s grip on how knowledge flows through a company weakens. That’s why this seemingly “plumbing‑level” story is strategically important.
4. The bigger picture
Glean fits into a broader shift from “AI features” to “AI infrastructure.” In 2023–2024, we saw the rise of vector databases, retrieval‑augmented generation (RAG) frameworks and orchestration tools promising to connect LLMs to enterprise data. Most of those were developer‑centric. Glean is effectively a higher‑level bet: that large organisations will prefer a packaged, opinionated layer that bundles retrieval, identity, permissions and model routing.
History suggests there is room for such layers. Identity and access management was once a side feature; now Okta and others own that neutral space across clouds and apps. Integration was once custom scripts; then Mulesoft and Zapier turned “connect everything” into a category. Data warehousing saw a similar shift with Snowflake. In each case, the neutral middle player survived despite platforms trying to do everything themselves, because enterprises valued independence and consistency across stacks.
The difference this time is the power of the incumbents. Microsoft already controls both the productivity surface (Office, Teams) and much of the underlying cloud. Google has Workspace, Cloud and Gemini. Both are racing to wire AI directly into every action an employee takes. If they can match Glean’s depth of connectors and governance — a big “if” — the argument for a separate intelligence layer gets weaker.
At the same time, the model landscape is fragmenting, not consolidating. Open‑source models are catching up for many workloads, and specialised models for code, legal, or healthcare keep appearing. That fragmentation actually strengthens the case for an abstraction layer that can route tasks intelligently and enforce consistent policy.
5. The European / regional angle
For European enterprises, Glean’s pitch hits several pressure points at once: data protection, sovereignty, and regulatory scrutiny under GDPR, the Digital Services Act and the upcoming EU AI Act.
A neutral intelligence layer can make it easier to enforce data‑minimisation, logging and access‑control obligations across dozens of SaaS tools. If every app ships its own assistant with its own half‑baked permission model, compliance officers are looking at a governance nightmare. A single layer that knows who is allowed to see what — and can prove it with auditable traces and citations — aligns much better with how European regulators think.
But it also raises questions. Where is the layer hosted? How is data moved between models, especially if some run outside the EU? Does the provider qualify as an “AI system provider” or a processor under the AI Act, and what additional risk‑management duties does that trigger? European buyers will ask these questions earlier and louder than their US counterparts.
There is also competitive pressure from within the region. Players like Aleph Alpha in Germany or Mistral in France are pushing for European‑controlled AI stacks, often with on‑prem or sovereign‑cloud deployment. Traditional enterprise search and knowledge‑management vendors in Europe, from Sinequa to local system integrators, are also racing to bolt LLMs onto existing deployments. Glean may be compelling for multinationals operating on US‑centric SaaS stacks, but it will face a more fragmented, regulation‑heavy and sovereignty‑sensitive market in Europe than at home.
6. Looking ahead
The next 18–24 months will show whether “intelligence layer” becomes a durable category or just a transitional step before platform consolidation. Expect three battlefronts.
First, depth of integration. It is relatively easy to build connectors that read from SaaS tools; it is much harder to enable safe, auditable write actions — updating tickets, changing CRM records, modifying access rights. Whoever can offer trustworthy AI agents that act across systems without breaking compliance will have a real moat.
Second, economics. Glean’s model is attractive partly because it doesn’t require frontier‑lab‑level compute spending. But as usage scales, customers will push hard on cost per task and per user. The ability to dynamically route workloads to cheaper open‑source or domain‑specific models, while keeping premium models for complex cases, will be a core differentiator.
Third, politics. Microsoft and Google will not sit quietly while a neutral layer intermediates access to their suites. Watch for tighter licensing terms, “better together” pricing bundles, or technical advantages given to their own assistants. On the other side, watch for cloud‑agnostic alliances: Glean‑style platforms partnering with security vendors, observability tools and open‑source model providers to present a credible alternative.
For buyers, the key question is lock‑in. Any AI deployment that hard‑wires you to a single model or suite in 2026 is a bet that today’s winner will still be best — and regulator‑friendly — in five years. That feels optimistic.
7. The bottom line
Glean is making a smart, if risky, bet: that the most valuable real estate in enterprise AI is not the chat window, but the invisible layer that understands who you are, what you’re allowed to see and which model should help you. If it can stay truly neutral, keep ahead on connectors and governance, and prove its value to security teams, it has a shot at becoming the Okta or Snowflake of the AI era. The open question is whether enterprises will protect that neutrality — or trade it away for the convenience of an all‑in‑one suite.
