
Skan AI, a process intelligence startup, raised $63 million in Series C funding to develop a platform that records how enterprise workers actually perform their jobs and then feeds that data to AI agents for automation.
The company's approach has delivered measurable results: at one U.S. bank, its AI agents reduced transaction costs by 32% and increased throughput by 41%, generating $18 million in annual savings.
With customers including a quarter of the Fortune 50 and major banks and insurers, Skan AI plans to expand into financial services, healthcare, and insurance sectors.
What happened
Skan AI, a process intelligence company founded in 2019, raised $63 million in a Series C round led by Cathay Innovation and Dell Technologies Capital. The company's platform records how enterprise work actually gets done by capturing anonymized metadata from employee desktops, then feeds that data to AI agents to automate tasks.
Why it matters
In a deployment at a large U.S. bank, Skan AI tracked 11.2 million context switches across 1,500 finance staff and identified $37 million in operational friction. The resulting AI agents cut cost per transaction 32% and lifted throughput 41%, worth $18 million in annualized savings. The company reports cumulative measured customer value of more than $500 million across its customer base, which includes a quarter of the Fortune 50, seven of the 10 largest U.S. banks, and three of the five largest U.S. insurers.
What to watch
Skan AI plans to use the funding to expand into financial services, insurance, healthcare, and technology sectors. The company has achieved revenue growth of more than 300% year-over-year, with net dollar retention averaging 150%, and has logged upward of 25 billion work signals to date. Total funding raised since 2019 is roughly $120 million.
Skan AI, founded in 2019, announced a $63 million Series C funding round aimed at expanding its platform that maps enterprise work and feeds that intelligence to AI agents. The company's core innovation is its approach to data collection: desktop software records employee activity via screenshots, but processes images locally on each machine. Only anonymized metadata—which applications were used, the sequence of steps, and decision points—travels back to Skan AI's analytics platform, ensuring that actual work product never leaves the customer's environment.
The platform consists of three complementary products. Blueprint visualizes how processes flow across systems and teams. Intelligence identifies operational bottlenecks and cost leaks. Agents, the automation component, deploys AI workflows based on patterns extracted from the company's highest-performing workers. In a deployment at a large U.S. bank, the results were substantial: Skan AI tracked 11.2 million context switches among 1,500 finance staff and surfaced $37 million in operational friction. AI agents built on those observations reduced cost per transaction by 32% and increased throughput by 41%, delivering $18 million in annualized savings according to the company.
Skan AI's growth trajectory reflects strong market traction. The company reported revenue growth of more than 300% year-over-year and net dollar retention averaging 150%. Its platform has logged upward of 25 billion work signals. The customer base includes a quarter of the Fortune 50, seven of the 10 largest U.S. banks, and three of the five largest U.S. insurers. Mitie Group plc, a U.K. facilities management company, is among the customers; its Chief Technology and Digital Officer, Cijo Joseph, credited Skan AI with providing "unprecedented operational visibility" that accelerated AI rollout. Across its customer base, Skan AI reports average operational savings of 30% to 40% per customer, with cumulative measured customer value exceeding $500 million.
The Series C was co-led by Cathay Innovation and Dell Technologies Capital, with participation from Citi Ventures, Bloomberg Beta, State Farm Ventures, and Wipro Ventures. Dell Technologies Capital, which also led Skan AI's $40 million Series B in March 2022, reinforced its backing. Cathay Innovation partner Simon Wu framed enterprise work context as becoming an infrastructure layer for corporate AI, comparing it to how customer relationship management software became the system of record for customer data. He noted that Skan AI is the only company he has observed building that context from direct observation rather than documentation or system logs. Dell Technologies Capital's managing director, Raman Khanna, emphasized enterprise leaders' mandate "to identify where AI can create measurable operational advantage." Skan AI plans to deploy the new capital toward product development and deeper penetration into financial services, insurance, healthcare, and technology sectors. Total funding raised since 2019 now stands at roughly $120 million.
Skan AI's Series C funding reflects growing enterprise appetite for AI agents that can be grounded in real operational data rather than generic automation rules. The company's founding principle—observing actual work rather than relying on documentation or system logs—addresses a practical gap in enterprise AI deployment. By capturing anonymized process metadata from employee desktops, Skan AI creates what investors and customers increasingly view as infrastructure for corporate AI: a detailed map of how work actually flows across systems and teams.
The company's customer roster and financial performance underscore the value proposition. With a quarter of the Fortune 50 already using the platform, combined with major representation among U.S. banks and insurers, Skan AI has demonstrated ability to scale within highly regulated, process-intensive sectors. The $18 million in annualized savings at a single bank deployment—driven by 32% cost reduction and 41% throughput lift—provides concrete evidence that AI agents informed by real operational intelligence can deliver measurable business impact. Cathay Innovation's framing of enterprise work context as an infrastructure layer for corporate AI, analogous to how CRM software became the system of record for customer data, suggests the market sees this as a category-defining positioning rather than a point solution.
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