Optimize
Agents
5
We just crossed a fun milestone at Scribe — $100M in revenue — on the heels of launching Scribe Optimize, Agents, and our MCP, and closing our Series C two quarters ago.
More interesting is the underlying shift it represents — one that will reshape how AI actually works inside enterprises.
Here’s the fundamental problem we’ve been obsessed with since day one at Scribe:
Organizations don’t actually know how they work.
They think they do. At least, at a vague, high level. But they don’t know how work actually gets done at the level where value is created.
Why? Because an organization’s most valuable asset — its expertise — stays trapped in people’s heads as institutional know-how.
That’s the context layer. It’s all the tacit knowledge, “this is how we actually do it” workarounds, and judgment calls. For decades, people compensated for the gaps. They’d tap a colleague for help, navigate ambiguity, and learn by osmosis. This way of working wasn’t always clean, but it held well enough.
Then, remote work exposed more of us to this context gap. (It’s much harder for people to absorb how a business runs from a distance.)
Now, AI makes this gap existential.
Because when AI fills in the gaps, things get dicey. It doesn’t know your exceptions, edge cases, unwritten rules, or ways of working. So it either produces generic, surface-level output OR it confidently gets things wrong.
This is the root cause behind why so many AI initiatives stall after pilots: the models aren’t taught enough about the business.
This is not an oversight — it’s because the data doesn’t exist in a format accessible to AI.
The context just isn’t there.
To make AI actually work inside enterprises, something fundamental has to change. The “how work actually happens” layer needs to move from:
- Implicit → explicit
- Fragmented → unified
- Human-held → system-accessible
That layer is what we call workflow context.
And it’s fast becoming a new requirement in the enterprise stack.
How Scribe solves for the enterprise context layer
Scribe built the workflow context layer for the enterprise. So your organization’s workflows are legible, structured, and usable by both humans and AI.
We started with a simple premise: If you can’t see how work happens, you can’t improve it. And AI definitely can’t operate without it.
From that, we built three core capabilities:
1. Make your expertise visible
Scribe Capture turns tacit knowledge into structured, shareable workflows.
This is how organizations finally understand how work gets done. It’s also how you spread best practices, level up the work across teams, and adapt faster to new processes and ways of working.
2. Improve how work actually happens
Scribe Optimize is our agentic AI product that’s built on your real workflows.
It gives you full visibility across your business, so you can zoom in, cross-cut, and actually see:
- Where workflows break
- Where to automate
- Where AI is underutilized
- Where capabilities are missing
And it pairs that with data-backed ROI — so you know what to improve, how to do it, and what it’s worth.
3. Give AI what it needs to operate inside your business
Through our MCP, Scribe delivers your structured workflow context directly into your AI systems.
Without context, the most sophisticated models “reason” by guessing. That’s potentially acceptable when it’s drafting an internal email, but not when it’s approving contracts, closing your books, or completing any other critical workflow.
Scribe’s MCP provides accurate, relevant, and up-to-date workflow context. That’s how you upgrade from AI as a tool to AI being a fully functioning business system.
The future of enterprise AI is built on context
Most companies aren’t limited in their AI ambitions — and models have improved so much in recent quarters that they’re not a constraint either. But companies are held back by AI that has no idea how their business actually works.
Closing the gap between what AI promises and what it's actually delivering is one of the most important problems in enterprise AI today.
Context closes the AI deployment gap and turns raw model capability into real-world execution.
Every major shift in enterprise technology has required a new layer. That’s how we got systems of record, systems of engagement, and integrations.
AI is no different. It needs the workflow context layer.
Without it, AI stays stuck in demos, pilots, and isolated wins. With it, AI lives up to the hype and can start the real work of transforming your organization.
A thank you
Building Scribe is the privilege of my lifetime.
The best part is hearing from customers. All the stories of problems solved, improvements made, and hours reclaimed. Every story has the same throughline: Scribe gives people their energy back. We set out to make work better. And we are. I see it inside the industry reports that place our NPS in the top 3% for enterprise software, and I feel it in the handwritten customer thank-you note I keep on our office wall. I’m so grateful for all of it.
Today, more than 6 million users across 150+ countries — including 94% of the Fortune 500 — rely on Scribe. I’m proud that leading enterprise companies like AstraZeneca, Havas Media, Klaviyo, Mindbody, Moody’s, New York Life, The Options Clearing Corporation, Okta, and Volvo are putting Scribe at the center of their AI strategies.
I’m incredibly thankful for our customers and the entire Scribe team, and I can’t imagine a better group of people to build the future with.
Onwards!
Jennifer
P.S.
If you want to be part of building the new context category and unlocking AI’s full potential for enterprise, we’re hiring.
