AI Startup Funding Surge Targets Code, Compliance, and Enterprise Friction

AI is moving into the places where business risk hides: failed code checks, missed compliance details, and wasted effort between enterprise systems. Three startups are putting fresh funding behind that shift, turning AI into an assurance layer that tests work, observes operations, and catches costly problems before they grow.

Blacksmith leads the latest wave with a $45 million Series B at a $550 million valuation. Peak XV Partners led the round, while existing investors Y Combinator and GV also participated. The San Francisco startup has expanded from roughly 800 customers to more than 6,000 companies, and weekly continuous integration jobs on its platform have grown between 5% and 10% week over week since the start of 2026.

Blacksmith Takes Aim at the Hardest Part of AI Coding

Writing software has become easier with AI tools, but checking that software remains a major challenge. “Writing code has gotten dramatically easier. Validating it hasn’t,” said Aditya “JP” Jayaprakash, Blacksmith’s co-founder and CEO.

Blacksmith’s platform helps companies build, test, and verify software before production. Its new product, codesmith, is a cloud coding agent designed to diagnose and autofix continuous integration failures, giving development teams an AI system focused on the checks that stand between a code change and a production release.

Aditya “JP” Jayaprakash founded Blacksmith in 2024 with Aayush Shah and Aditya Maru. The company raised a $3.5 million seed round in May 2025, led by GV and Y Combinator, then announced a $10 million Series A on September 18, 2025. That Series A closed in 14 days, and Blacksmith had passed $1 million in annual recurring revenue with about 800 customers.

The company raised its Series B in March 2026 and announced it five months later. Its valuation climbed from $60 million to $550 million in less than a year, while its largest customers began spending more than $1 million a year on the platform. Blacksmith claims more than $500 million in cumulative customer value, representing expected savings.

Skan AI Turns Enterprise Work Into an AI Reasoning Layer

Skan AI is applying a similar idea to business operations. The enterprise AI startup raised $63 million in Series C funding co-led by Cathay Innovation and Dell Technologies Capital, bringing its total funding to roughly $120 million. Citi Ventures, Bloomberg Beta, State Farm Ventures, and Wipro Ventures also invested.

Led by CEO Avinash Misra, Skan AI observes employee work across enterprise software through desktop monitoring. Its technology deploys observation tools on employee desktops to model business processes, focusing on the work that happens between system states rather than relying only on system logs.

That distinction matters because enterprise friction often lives between clicks, handoffs, and decisions that standard software records never capture. Skan AI abstracts observed data into a context model for AI reasoning, then aggregates the data to protect privacy by surfacing statistical patterns instead of individual behavior.

The approach has been approved by European works councils and remains within enterprise firewalls. Skan AI’s products now include Skan AI Blueprint, Skan AI Agents, and its existing Skan AI Intelligence platform.

At one U.S. bank, Skan AI observed 11.2 million context switches and uncovered $37 million in operational friction. Its technology helped cut cost per transaction by 32% and lift throughput by 41%, saving $18 million annually. For insurers, the platform produced roughly a 25% productivity uplift in core claims processes, and it has helped some customers reduce headcount in certain processes.

Skan AI claims more than $500 million in customer value, representing expected savings and interventions. Yet the company’s method raises a direct privacy challenge. Delphine Icart warned, “This sounds like a great idea, but you are dead on arrival. You are observing things that you shouldn’t be observing — the privacy of my operators, and the sovereignty of my data on those screens.”

Dili Brings Real-Time AI to Compliance

Dili is targeting another expensive business problem: compliance monitoring across energy, infrastructure, construction, and manufacturing projects. The company raised $21.7 million in total funding, including a $15 million Series A led by Khosla Ventures, with participation from Y Combinator’s Garry Tan, Allianz, Brick and Mortar Ventures’ Darren Bechtel, and Rebel Fund. Its total funding is roughly $58.5 million.

Dili’s platform checks 100% of project data in real time. It has processed more than $1.4 billion in wages, 5.2 million labor hours, and nearly 16,000 certified payroll reports for customers including EDF, Radiance, Heelstone, and Borea.

The results point to the value of replacing samples with complete data checks. Dili reduced review time from more than seven hours to under five minutes per organization and uncovered $6 million in IRS penalty exposure for one customer before it became a liability. The platform has caught or prevented more than $50 million in fines and clawbacks.

“Compliance in this industry has always meant hoping the sample your auditor pulled happens to be clean. We look at everything, every report, every wage determination, every week,” said Anand Chaturvedi, Dili’s CEO. Vinod Khosla, founder of Khosla Ventures, said America is in the middle of the largest infrastructure buildout in a generation and described Dili as a real-time, AI-powered assurance layer for catching problems before they become liabilities.

Across code pipelines, desktop workflows, and project records, these startups are building AI systems that do more than generate content. They validate, observe, reason, and intervene at the point where errors become expensive. The next phase of enterprise AI may be defined not by what systems create, but by what they stop from going wrong.

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