OpenAI-Backed Worktrace AI Raises $9M to Automate Enterprise Workflows: Former Meta Engineers Launch Agent Platform That Studies Companies' Internal Processes

Worktrace AI, a stealth startup founded by former Meta and OpenAI engineers, has emerged with $9 million in seed funding to tackle one of enterprise automation's biggest challenges: understanding and automating complex internal workflows that vary dramatically between organizations.

The December 13, 2025 funding round was led by Conviction and 8VC, with participation from OpenAI's startup fund and prominent AI leaders including former OpenAI CTO Mira Murati. This backing signals significant confidence in Worktrace's approach to workflow automation that goes beyond traditional robotic process automation (RPA) tools.

Revolutionary Approach to Workflow Understanding

Unlike existing automation platforms that require extensive manual configuration, Worktrace AI's platform studies companies' internal workflows automatically, learning how different departments, teams, and individuals actually work rather than relying on documented processes that often don't reflect reality.

Co-founder Angela Jiang, who previously led AI initiatives at Meta, explained that traditional automation tools fail because they assume workflows are standardized and documented. "Most companies have these intricate, undocumented processes that have evolved organically over years," Jiang noted. "Our AI agents observe and learn these patterns, then suggest automation opportunities that actually match how work gets done."

Addressing the Automation Implementation Gap

The startup emerges at a critical moment when enterprises are eager to deploy AI automation but struggle with implementation. McKinsey research indicates that while 87% of companies want to automate workflows, only 31% have successfully deployed automation beyond pilot projects, primarily due to the complexity of mapping and configuring real-world business processes.

Worktrace AI's approach addresses this gap by removing the burden of process documentation and manual configuration from enterprise customers. The platform integrates with popular business tools like Slack, Salesforce, Notion, and Google Workspace to observe workflow patterns, then automatically suggests and implements automation workflows.

Experienced Leadership from AI Giants

The founding team brings deep experience from organizations at the forefront of AI development. Co-founder Deepak Vasisht previously worked on infrastructure systems at OpenAI, while Angela Jiang led machine learning initiatives for Meta's business tools division.

This combination of enterprise AI experience and infrastructure expertise positions Worktrace to handle the technical challenges of large-scale workflow automation, from data security and integration complexity to the AI reasoning required for process understanding.

Market Validation from Industry Leaders

The participation of OpenAI's startup fund and former OpenAI executives like Mira Murati provides significant validation for Worktrace's approach. OpenAI has been selectively investing in startups that complement rather than compete with its core language model offerings, suggesting they see workflow automation as a crucial application layer for AI technology.

Conviction partner Sarah Guo, who led the investment, emphasized the market opportunity: "Enterprise workflow automation is still largely manual because existing tools require too much upfront configuration. Worktrace's approach of learning organizational patterns first, then automating second, could finally unlock the automation potential that companies have been seeking."

Competitive Landscape and Differentiation

Worktrace enters a crowded automation market that includes established players like UiPath, Microsoft Power Automate, and Zapier, as well as emerging AI-native platforms. However, the company's focus on workflow discovery rather than just workflow execution represents a significant differentiation.

While competitors require users to define automation workflows manually, Worktrace AI observes actual work patterns to suggest and implement automations automatically. This approach could significantly reduce the time and expertise required for automation deployment, potentially expanding the market beyond technical teams to include business users.

Future Workforce Implications

The emergence of Worktrace AI reflects a broader trend toward AI systems that can understand and optimize human work patterns without requiring extensive manual programming. This evolution from rule-based automation to AI-driven workflow optimization could accelerate the pace at which routine knowledge work becomes automated.

For enterprise workers, platforms like Worktrace represent both opportunity and disruption. While the technology can eliminate repetitive tasks and improve efficiency, it also provides organizations with unprecedented visibility into work patterns and automation opportunities that could reshape job requirements across departments.

The $9 million funding enables Worktrace to build out its engineering team and begin pilot deployments with enterprise customers. Industry observers will closely watch these early implementations to gauge whether AI-driven workflow discovery can finally bridge the gap between automation potential and practical deployment in large organizations.