Skan AI, a Menlo Park startup that builds AI agents by watching how employees actually do their jobs, announced a $63 million Series C funding round on August 12, 2026. The round was co-led by Cathay Innovation and Dell Technologies Capital, with participation from Citi Ventures, Bloomberg Beta, State Farm Ventures, and Wipro Ventures. The company also made two new products, Skan AI Blueprint and Skan AI Agents, generally available, completing a three-part platform built on what it calls a "context graph of work."
The funding brings Skan AI's total capital to approximately $120 million. The round is a reunion of existing backers rather than a changing of the guard. Cathay Innovation led the company's $14 million Series A, and Dell Technologies Capital led the $40 million Series B, which also included GSR Ventures and Liberty Global Ventures. Citi Ventures has participated in all three rounds. Notably, State Farm Ventures and Citi Ventures were Skan AI customers before they became investors.
Watching Work, Not Just Logs
Skan AI's core thesis is that enterprise AI agents fail in production because they are grounded in process documentation and system logs rather than in the work itself. The company's software observes employee desktops across applications, including CRM systems, email clients, and mainframe sessions. That observed data is distilled into a continuously updated model of how a business runs, and that model grounds the AI agents Skan builds and operates for customers.
The platform has processed more than 25 billion work signals, according to the company. Skan AI counts seven of the ten largest U.S. banks as customers, along with a quarter of the Fortune 50. The platform runs on NVIDIA AI Enterprise and NIM microservices.
Avinash Misra, co-founder and CEO of Skan AI, framed the company's ambition in a VentureBeat interview. "Everyone is obsessed with building a better car. We think the bigger opportunity is building a better navigation system," he said.
A Bank's Numbers Tell the Story
Skan AI provided detailed results from one top U.S. bank. The observation software tracked 11.2 million context switches across 1,500 finance professionals. That analysis uncovered $37 million in operational friction. After Skan AI's intervention, the bank reduced cost per transaction by 32 percent and increased throughput by 41 percent, producing $18 million in annualized savings.
The company claims more than $500 million in cumulative customer value. Misra clarified that this figure represents identified savings opportunities, not all banked savings. Skan AI also claims revenue rose more than 300 percent year over year for a second consecutive year, and it reports an average net dollar retention of 150 percent. These figures come from the company and a VentureBeat interview; they are not independently verified.
Simon Wu, a partner at Cathay Innovation, described the investment as a bet on infrastructure. He said enterprise work context is becoming "the foundational infrastructure layer for enterprise AI, the same way CRM became the system of record for customer relationships."
Privacy and Headcount Tensions
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The observation-first model carries an obvious tension regarding privacy. Misra addressed the concern directly in the VentureBeat interview. Skan AI aggregates patterns across hundreds of workers rather than profiling individuals. The company scopes monitoring to opt-in applications and URLs, and it keeps data inside the enterprise firewall. The architecture has passed review by European works councils.
Misra also acknowledged that the technology has led some customers to cut headcount in certain processes. In one anti-money-laundering operation, AI agents now run 60 percent of cases. The company's pitch has evolved with the market, shifting from computer-vision process discovery, to process intelligence, to the context layer for agentic AI.
NVIDIA's global head of banking, who was quoted in the funding announcement, spoke to the appeal of agents that run on infrastructure a financial institution owns. The unnamed executive's comments underscored the platform's enterprise focus.
A Founders' Journey From Kanpur
Skan AI was founded in 2018 by Misra and Manish Garg. The two are childhood friends from Kanpur, India, and later classmates at the Indian Institute of Technology. Their first venture was acquired by Genpact. That early exit gave them the experience to build again, this time with a focus on how work actually happens.
The company cites Gartner research that only 8 percent of enterprises have AI agents in production. Gartner also projects that 95 percent of early implementations will require a complete redesign. Skan AI positions its context graph as the answer to that failure rate, grounding agents in observed reality rather than in static documentation.
The Road Ahead
The Series C arrives as the market for agentic AI heats up. Skan AI's customer-turned-investor dynamic, with State Farm and Citi both backing the company, signals strong confidence from the enterprises that use the product. Misra flagged that endorsement as the one he values most.
The company's three-part platform now includes the observation layer, the context graph, and the agents themselves. With $120 million in total funding and a growing customer base among the largest U.S. banks, Skan AI is betting that watching work is the key to automating it.
The article was published by Unite.AI and written by Evan Mercer, an AI-generated correspondent. The publication includes a disclaimer that articles by Evan Mercer are AI-generated and reviewed by Unite.AI's editorial team.