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[Blog post](/blogs)12 August 20265 mins

# The Context Enterprise AI Is Missing: How Work Actually Gets Done

Today, we're announcing our $63 million Series C, co-led by Cathay Innovation and Dell Technologies Capital, with participation from State Farm Ventures, Bloomberg Beta, Wipro Ventures, and Citi Ventures.

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Avinash Misra](/authors/avinash-misra)

-   [Context Graph of Work](/blogs/tag/context-graph-of-work)
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My co-founder Manish Garg and I founded Skan AI because we knew that if we could observe every part of the work being done in a company, there would be no end to the benefits they could gain by building AI on top of that foundation. In the years since, we’ve seen the largest banks, healthcare and insurance companies embrace this vision by becoming our clients.

Today, we're announcing our $63 million Series C, co-led by Cathay Innovation and Dell Technologies Capital, with participation from State Farm Ventures, Bloomberg Beta, Wipro Ventures, and Citi Ventures. But more than the funding, I want to share what we've learned, because what's happening in enterprise AI right now is exactly what we spent seven years preparing for.

#### **The problem nobody is addressing**

Our clients tell us that they've run AI pilots to try to improve how one part of the organization does their work - that could be claims resolution, mortgage quality insurance, or underwriting, to name a few that we've seen. But when they try to actually deploy that pilot into production it doesn't work as expected.

We've spent the past seven years thinking about why these pilots fail. It turns out that you can build a pilot without an agent fully understanding how work is being done in the company, but you cannot push that agent into production without it truly understanding the work.

One Fortune 500 insurer believed its documented process covered more than 95 percent of cases, but our observation showed it covered only two-thirds. But the more important insight wasn't the size of the gap, it was the impact of it.

In complex, regulated workflows, the cases that fall outside documented processes determine regulatory compliance, customer outcomes, and operational risk. A single percent of uncaptured context, compounded across thousands of daily decisions, is the difference between an agent that performs and one that fails where it matters most.

#### **Why traditional approaches fail**

On the most basic level, the reason why nobody had figured out how to close this gap is because it’s incredibly difficult to do so. Interviewing people captures how they remember doing the work, not how they actually did it. And even watching people work has its limits: observer a handful of employees, or your best performers, and you get a sample - not the truth. Every enterprise runs on thousands of small variations nobody thinks to mention, because nobody asked, and nobody was watching everyone, all the time.

But until we built Skan, companies had to rely on these flawed methods. Now they don’t have to. Because Skan AI observes how work actually gets done across every role, every system, every application, across an entire enterprise. Not a sample. Not the best performers. Everyone. That scale is what turns observation into organizational truth.

You cannot fix a source data problem downstream. Better models will not solve it. Better prompts will not solve it. Better retrieval pipelines will not solve it.

#### **The platform we built**

We now know that for AI agents to work successfully, they need to be provided with structured context, what many people call a context graph. At Skan, we call this the Context Graph of Work. It’s a living, continuously updated record of how work actually gets done, and unlike the generic context graphs others are building, it belongs entirely to the enterprise that built it, not to us or anyone else. The Context Graph of Work is what powers three products that function as a continuous cycle, each one feeding the next.

-   **Skan AI Blueprint** discovers and prioritizes where AI will pay off - across every system people use, including the legacy environments and regulated workflows most tools can't reach. It shows exactly where agents will create value before spending a dollar on deployment.
-   **Skan AI Intelligence** captures how work actually gets done - the decisions your best people make differently, the exceptions they handle, the institutional knowledge that usually walks out the door when someone leaves. It turns that into the standard every agent follows.
-   **Skan AI Agents** executes against that context autonomously - built from thousands of observed real cases, tested against reality before deployment, and continuously updated as the business evolves. Governance is designed in from day one: human oversight, policy controls, and a full replay of every action the agent takes.

Blueprint informs Intelligence. Intelligence powers Agents. Agents feed what they learn back into Blueprint. The Context Graph gets smarter over time.

#### **The human dimension**

What I've learned along the way is that when AI finally understands how work gets done, everyone gets better at their job.

The claims adjudicator who used to spend half their day on routine exceptions now focuses on the cases that actually need their judgment. The new hire who used to take six months to reach full productivity gets there in weeks. The operations leader who used to wonder whether the process was running correctly now knows.

That's what we built Skan AI to deliver - AI that works in the real world, on real work, for real people.

#### **Thank you**

A quarter of the Fortune 50 and seven of the ten largest U.S. banks use Skan AI today. Together they have realized over $500 million in value. It is especially meaningful that State Farm Ventures and Citi Ventures have become investors after first being customers. That kind of trust - from organizations that have seen what Skan AI does from the inside - means more to us than any headline number.

To the entire Skan AI team: thank you for building something real. To our customers, investors, and partners: thank you for believing in what we saw seven years ago.

We can't wait for what's next.

If you're deploying AI for complex enterprise operations, we'd love to talk. And if you're interested in helping define how enterprise AI works in production, we're hiring.

_Watch the full announcement: \[[skan.ai/skan-ai-raises-series-c](https://skan.ai/skan-ai-raises-series-c)\]_

_Request a demo: \[[skan.ai/request-demo](https://skan.ai/request-demo)\]_

_See what Dell Technologies Capital is saying: \[[https://www.delltechnologiescapital.com/resources/skanai-series-c](https://www.delltechnologiescapital.com/resources/skanai-series-c)\]_

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[Avinash Misra](/authors/avinash-misra)

Founder & CEO

Avinash Misra is a serial entrepreneur and the co-founder & CEO of Skan AI. He is passionate about helping organizations gain a deep understanding of their business processes, using AI to drive continuous process optimization and effective digital transformation. Previously, he co-founded Endeavour, a pioneer in enterprise mobility, which was acquired by Genpact in 2015. His entrepreneurial work has been defined by a drive to use technology to transform the enterprise - by making it more efficient.

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