Analysis
The Round
Skan AI raised $63 million in Series C funding co-led by Cathay Innovation and Dell Technologies Capital, with Citi Ventures, Bloomberg Beta, State Farm Ventures and Wipro Ventures also participating, according to VentureBeat. The round brings the seven-year-old company's total funding to roughly $120 million.
What Skan AI Builds
Skan builds what it calls a "context graph of work" by observing how employees actually perform their jobs across enterprise software, rather than relying on official process documentation that's often outdated or incomplete. Alongside the raise, the company is launching two new products -- Skan AI Blueprint and Skan AI Agents -- that combine with its existing Skan AI Intelligence offering into a platform for discovering, modeling and ultimately automating enterprise workflows.
The Thesis
CEO and co-founder Avinash Misra's argument is that the industry has misdiagnosed where enterprise AI actually fails: the underlying models are capable enough, but they're dropped into businesses without an accurate picture of how those businesses really operate. "Everyone is obsessed with building a better driver," Misra said. "We think the bigger opportunity is building a better navigation system." That framing positions Skan not as another AI-agent vendor but as an infrastructure layer that other agent vendors could plug into.
Company Background and Customers
Skan is trusted by a quarter of the Fortune 50, according to the company, spanning financial services, insurance and other large enterprises where legacy processes are especially undocumented and fragmented. Seven years old, the company predates the current generative-AI boom, having originally built its work-observation technology for process-mining use cases before repositioning around AI-agent grounding as that category emerged.
The Competitive Field
Skan competes for enterprise attention against process-mining incumbents like Celonis and UiPath's process-intelligence tools, as well as against AI-agent platforms including Decagon and Sierra that build their own context layers internally rather than buying one externally. Skan's bet is that context-building is valuable and hard enough to become its own standalone category, rather than a feature every agent vendor builds in-house -- the same wager Blacksmith is making in code validation and Infinimmune is making in antibody discovery, each betting a narrow, hard problem deserves its own dedicated vendor.
The Counterweight
A seven-year-old company on its Series C with roughly $120 million raised total is a more modest trajectory than the AI-native startups posting 10x valuation jumps in under a year -- Skan's pitch depends on enterprises recognizing work-context as a distinct budget line, and large incumbents like Celonis or the AI-agent vendors themselves could bundle comparable observation capabilities in as a feature rather than paying a separate vendor, the same competitive risk facing most infrastructure-layer startups in this cycle.