Pit launches ai-native enterprise platform with $16 million funding led by a16z

Pit, an AI-native platform designed to replace fragmented enterprise tools such as spreadsheets, inboxes, and rigid SaaS systems, has officially launched alongside $16 million in funding led by Andreessen Horowitz (a16z). The round also included Lakestar, executives from OpenAI, Anthropic, Google, Deel, and Revolut, as well as members of the Stena and Lundin families.

The company is positioning itself as an “AI product team as a service,” allowing businesses to design and deploy custom, production-grade software tailored to their internal operations. Instead of adapting workflows to existing tools, companies can now build systems designed around how they actually operate.

Across industries, enterprise operations remain heavily fragmented despite more than $1 trillion spent on digital transformation. Many organizations still rely on manual processes and disconnected systems that limit scalability and efficiency.

Pit aims to replace this outdated layer with AI-native software that is generated around each company’s workflows. The platform is designed to improve speed, reduce operational friction, and eliminate dependency on legacy SaaS structures.

“We have always believed enterprise software should adapt to companies, not the other way around. AI finally makes that possible,” said Pit CEO and co-founder Adam Jafer.

Pit Studio builds enterprise systems

Pit Studio serves as the core intelligence layer of the platform, designed to learn how organizations operate and translate those workflows into fully functional production systems. It removes the need for manual configuration by generating software directly from business behavior.

The system continuously adapts to operational inputs, allowing companies to deploy internal tools that evolve alongside their processes. This approach reduces dependency on static systems that require constant reconfiguration or third-party customization.

Pit Cloud powers secure infrastructure

Pit Cloud provides the governed infrastructure layer that supports enterprise deployment at scale, including tenant isolation, ISO 27001 compliance, SSO, role-based access control, and full audit observability. It is designed to meet enterprise security and governance standards.

Unlike low-code platforms or AI assistants that support tasks, Pit delivers fully operational systems running live business processes. This positions the platform as a replacement for entire software stacks rather than a productivity layer on top of them.

The platform is already in enterprise pilot use across logistics, telecom, e-commerce, and healthcare, with companies such as Voi, Tre, Stena Recycling, and Kry among early adopters. Some deployments have reportedly gone live within days or weeks.

Early results include up to 85 percent reduction in campaign execution time, more than 10,000 hours saved annually per deployment, and 99 percent invoice acceptance rates through automation. One large industrial deployment eliminated validation errors entirely using real-time AI processing.

“Every AI company is selling speed. Pit is selling speed that holds up for years—secure, governed, and built to last,” said Alex Rampell, General Partner at Andreessen Horowitz.

Pit was developed by founders and engineering leaders from companies including Voi, Klarna, and iZettle, who previously built large-scale AI-driven operational systems. Their experience scaling automated workflows across enterprise environments shaped the platform’s design philosophy.

AI reshapes enterprise infrastructure

Pit represents a shift toward AI-generated enterprise systems where software is no longer configured but created based on real operational needs. This moves enterprise infrastructure away from static tools toward adaptive systems that reflect how organizations function.

The company positions itself as a new category in enterprise software focused on long-term, governed AI systems capable of running core business operations. This approach signals a broader transformation in how enterprises may design and manage internal technology stacks.