Arvind Jain

CEO @glean

[Arvind Jain @jainarvind]

https://t.co/isdpfDmbm3

[Arvind Jain @jainarvind]

@peejay291 Yes, @glean 😄

[Arvind Jain @jainarvind]

AI architecture discipline is AI cost discipline.

Standardizing on a single AI platform can feel appealing because it seems simpler. Over time, though, it often becomes more expensive, more constraining, and harder to govern.

To control AI costs:
- Use the right model for the right job
- Invest in a horizontal context layer
- Centralize governance instead of pushing it to the edge

[Arvind Jain @jainarvind]

@levie Our customers see the same thing. The highest-value context is spread across docs, chats, tickets, CRM, and all the informal ways work actually gets done. That’s where they find @glean helps most.

[Arvind Jain @jainarvind]

For decades, software was priced like seats. AI is starting to look more like labor. That changes how leaders need to think about architecture, ROI, and which work should be done by people vs. tokens.

[Deirdre Bosa @dee_bosa]

"This is the first time ever that I can remember that technology costs the same as people, and you're making that comparison: choose tech or people”-@jainarvind

[Arvind Jain @jainarvind]

This is why enterprise AI is not just a model problem. If AI does not understand your company’s people, systems, workflows, and permissions, it will not create much value.

Context is what turns intelligence into useful work.

[Tom Blomfield @t_blom]

Imagine replacing 90% of your employees with a team of geniuses who have no idea how your company operates.

Total chaos. Nothing works.

That’s what AI feels like today.

The missing piece is extracting all the domain knowledge from people’s heads and providing that as structured context to the models.

[Arvind Jain @jainarvind]

@AnsteyAsia @glean Thanks @AnsteyAsia, sent you a message.

[Arvind Jain @jainarvind]

I'm proud to share that @Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading.

We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems.

That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI.

That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions.

It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year.

And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency.

I enjoyed talking with @CNBC's @dee_bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context.

Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

[Arvind Jain @jainarvind]

@shaig @glean Appreciate the support, @shaig!

[Deirdre Bosa @dee_bosa]

LIVE at 12p PT / 3p ET: The AI bill is coming due

@glean CEO @jainarvind breaks a milestone with us and tells us what CFOs are saying about tokenmaxxing
@FactoryAI CEO @matanSF on model routing and cutting AI bills without cutting capability

Watch here:
https://t.co/LMXh06g41B

[Arvind Jain @jainarvind]

@dee_bosa @glean @FactoryAI @matanSF Great joining you, @dee_bosa, to talk about what I’m hearing from leaders on token spend and Glean’s $300M ARR milestone.

[Arvind Jain @jainarvind]

Can your AI answer these questions?
- Who owns this customer escalation?
- What changed on this project after last week's review?
- Why did we decide to push the launch?

@glean's Enterprise Graph connects people, projects, docs, and customers so AI can reason across how work actually gets done, instead of starting cold every time. More effective, less expensive.

For a long time, people have asked us if they could actually see the graph. Now you can.

[Tony Gentilcore @tonygentilcore]

We've talked about @Glean's Enterprise Knowledge Graph since 2019. People kept asking to see it, so we built a demo.

Ask Glean something like: "show me my manager's projects and the team behind them," and watch it traverse the graph in real time, mapping work and people across the org. Very cool!

[Arvind Jain @jainarvind]

Our team wrote a guide to token efficient AI architectures here: https://t.co/72Rqq0YwZJ

[Arvind Jain @jainarvind]

AI is becoming a real budget line in the enterprise.

The question isn’t who deployed the most copilots. It’s who gets the most useful work per token.

As AI becomes more agentic, token efficiency becomes an architecture issue.

In our benchmark testing with Claude Cowork, Glean’s remote MCP server was preferred ~2.5x more often than off-the-shelf MCP tools. Those tools used ~30% more tokens on average — and on winning outcomes, nearly 2x more: ~83K vs. ~43K.

The goal isn’t less AI. It’s more useful work per token.

[Arvind Jain @jainarvind]

@gartner @glean Gartner Subscribers can read the report here: https://t.co/tPTiFRIsgV

[Arvind Jain @jainarvind]

In a recent @Gartner note, Kevin Quinn and Radu Miclaus call @Glean “the company to beat” in enterprise context graphs.

Appreciate the rigor of the analysis, as this market is moving fast.

What stood out in Glean: strong enterprise adoption, deep integrations, and a trusted layer that unifies fragmented enterprise systems for AI agents.

Customers don’t want impressive demos. They want AI that understands how work actually gets done, respects permissions, and drives real outcomes.
Grateful for the recognition and for the customers pushing us forward.

[Arvind Jain @jainarvind]

@CNBC Check out the full list: https://t.co/b6w3Dskspn

[Arvind Jain @jainarvind]

Honored to see Glean named to @CNBC’s 2026 Disruptor 50.

What makes this recognition meaningful is what it reflects: companies using technology to challenge incumbents, create new categories, and reshape industries.

In the enterprise, the challenge is making AI actually useful. Grounded in company context, connected across fragmented systems, and trusted in real workflows. That’s the problem we’ve been focused on from day one at Glean.
Congrats as well to our customers @databricks, @vanta, @Whatnot, @Canva, @Cursor, and @AbnormalAI, plus our partners @AnthropicAI and @OpenAI. Thanks to CNBC for the recognition.

[Arvind Jain @jainarvind]

The center of gravity is shifting from the model layer to the operating layer around the model.

But inside real companies, the primary bottleneck is rarely raw model capability. It’s getting AI to operate reliably across fragmented data environments, inconsistent processes, permission structures, legacy systems, and workflows shaped as much by tacit knowledge as formal policy.

The competitive advantage is the context and operating layer that lets companies orchestrate, govern, and swap models without losing value.

[Arvind Jain @jainarvind]

Yes, companies still need AI usage policies, but this can’t rest on policy alone. Glean owns a big part of the enforcement layer with source-permission enforcement, private-by-design deployment, zero-retention model handling, and guardrails for sensitive data, prompt injection, and off-limits topics. The company sets policy; the platform should help enforce it so it’s not all on employees to get it right every time.

[Arvind Jain @jainarvind]

Agent sprawl has become a real concern for many leaders I talk with. Agents are popping up across the company without shared context, clear ownership, consistent guardrails, or a reliable way to know which ones are actually creating value.

The next phase of enterprise AI will be defined less by agent creation and more by agent operations, where testing, versioning, monitoring, and governance are built into the system from the start.

At @Glean, we think about that through the Agent Development Lifecycle (ADLC). It is a practical model for how enterprises move from promising demos to agents that are grounded in the right context, launched with the right controls, and improved over time.

Alongside the ADLC, we’re announcing new product capabilities designed to support that lifecycle end-to-end: from auto-mode agents and sub-agents to agent sandbox, agent library, agent access policies, and agent insights.

In the enterprise, success won’t come from building the most agents. It will come from building agents you can trust, govern, and improve over time.

[Glean @glean]

https://t.co/YkGyE6GeYR