There’s a gold rush happening in the corporate world, and its name is Enterprise AI. Every big company is frantically trying to bolt an AI brain onto its operations, hoping to unlock unprecedented productivity. But amidst this frenzy, a fundamental, and frankly, quite messy problem has emerged: most company data is a complete and utter tangle. This isn’t just about finding a file; it’s about making sense of decades of emails, Slack messages, presentations, and code scattered across dozens of systems. The big AI models from OpenAI or Google are incredibly powerful, but on their own, they have zero context about your business. They don’t know who your top salesperson is, what happened in last quarter’s planning meeting, or why “Project Nightingale” is a secret.
This is where the real, unglamorous, but critically important work begins. The race isn’t just about building the shiniest AI assistant; it’s about building the plumbing that makes it all work. We’re talking about an AI middleware layer – a kind of universal translator and traffic controller for enterprise data. And a company called Glean is making a bold, and incredibly well-funded, bet that this foundational layer is the most valuable piece of real estate in the entire AI landscape.
A Look Under the Bonnet: The AI Middleware Layer
So, what on earth is an AI middleware layer? Think of it like the central nervous system of your company’s AI strategy. On one side, you have the powerful, general-purpose brains—the large language models (LLMs) like GPT-4 or Gemini. On the other, you have your sprawling, chaotic collection of business applications: Salesforce, Microsoft 365, Slack, Jira, you name it.
The middleware sits squarely in between. Its job is not to be the brain, but to connect the brain to the corporate body in a smart, secure, and coherent way. It acts as a neutral infrastructure, abstracting away the complexities of which specific AI model you’re using and which data source you’re pulling from. It’s the essential but often invisible scaffolding that holds the whole structure up.
The Great Pivot: From Search Box to Strategic Foundation
Glean didn’t start its life here. Its journey reflects the broader enterprise search evolution. Initially, Glean was known for building a brilliant search tool for companies, one that could actually find the documents and conversations you were looking for. It was a genuine step up from the clunky, often useless search bars embedded in most corporate software.
But the team, led by CEO Arvind Jain, a distinguished engineer with stints at Google and Rubrik, saw the writing on the in-terminal wall. When Microsoft and Google started embedding their own AI assistants, Copilot and Gemini, directly into their ubiquitous productivity suites, competing on the “search box” level became a fool’s errand. Why fight a land war with giants on their home turf?
Instead, Glean pivoted. They realised their core strength wasn’t just the user interface; it was the powerful backend system they had built to index, understand, and map all of an organisation’s data. They were already building the plumbing. The logical next step was to position that plumbing as the essential foundation for any AI application a company might want to use.
The Bedrock of Integration Architecture
This move places Glean at the heart of integration architecture. Implementing AI effectively isn’t a simple plug-and-play exercise. You can’t just give an LLM an API key to your company’s Dropbox and hope for the best. A proper integration requires a thoughtful strategy for how data flows and how systems communicate.
The middleware approach offers a powerful solution by:
– Providing a single point of connection: Instead of building dozens of brittle, point-to-point integrations, an application can connect to the middleware layer to access everything.
– Enabling model agnosticism: Don’t want to be locked into OpenAI? The middleware can swap in a model from Anthropic or Cohere without you needing to rebuild your entire stack. As Jain wisely noted in a recent TechCrunch interview, “Our product gets better because we’re able to leverage the innovation that they are making in the market.”
– Creating a unified data view: The middleware ingests and understands data from all connected sources, creating a comprehensive “knowledge graph” of the company.
From Data Chaos to Contextual Clarity
This leads us to the crux of the problem: data unification strategies. This is where the Arvind Jain vision really shines. As he bluntly puts it, “The AI models themselves don’t really understand anything about your business.” They lack context.
Glean’s solution is to meticulously map this context. The middleware doesn’t just index files; it understands relationships. It knows which team works on which project, who reports to whom, what acronyms mean in your specific company, and which documents are the canonical, up-to-date sources of truth versus outdated drafts. This creates a rich, contextual layer that can be fed to any AI model, transforming its generic intelligence into genuinely useful, company-specific insight. When an employee asks, “What’s the latest on Project Nightingale?”, the middleware ensures the AI accesses the correct, most recent documents, understands the project’s context, and delivers a relevant answer.
The Gatekeeper: Governance and Permissions
Here’s the bit that should make every CISO and general counsel pay attention. In the rush to deploy AI, it’s frighteningly easy to create a security nightmare. What’s to stop an AI assistant from summarising a confidential HR document for an unauthorised employee?
This is perhaps the most critical function of an AI middleware layer: enforcing governance and permissions. Glean’s system is built to be “permissions-aware.” It crawls and respects all the existing access controls from the source systems. If you don’t have permission to see a file in Google Drive, you won’t see it in a summary generated by an AI using Glean’s middleware.
Furthermore, it tackles the dreaded “hallucination” problem head-on. Because the middleware has a direct line to the source material, it can ground every answer in verifiable fact. It doesn’t just give you an answer; it provides citations, linking back to the precise document, Slack message, or email from which the information was drawn. This verification loop is essential for building trust and ensuring accuracy in a business setting.
The Future According to Jain and the Market
The Arvind Jain vision is one of a neutral, indispensable layer in the enterprise stack. By refusing to pick a “winner” in the LLM wars and instead opting to be the Switzerland of AI infrastructure, Glean can work with everyone. This strategy is clearly resonating with investors, who recently poured a reported $150 million into the company in a Series F round, doubling its valuation to a staggering $7.2 billion, a testament to the market’s belief in this foundational approach as analysed by leading industry voices like Ben Thompson at Stratechery.
The major platform players like Microsoft and Google are building vertically integrated “walled gardens.” They want you to use their AI, their applications, and their cloud, all tied together. Glean is betting that many enterprises will prefer a more open, flexible approach, one that avoids vendor lock-in and allows them to adopt the best-of-breed models as they emerge.
This is the great strategic question facing CIOs today. Do you go all-in with a single provider, or do you build a more modular stack on top of a neutral middleware layer? Glean’s success suggests the latter argument is gaining significant traction. How is your organisation approaching this challenge, and are you confident in the foundations of your AI strategy?
