Potpie AI raises $2.2M to solve the enterprise context crisis blocking AI agent adoption
Potpie AI raised $2.2 million to build structured context layers that allow AI agents to operate effectively across enterprise-scale codebases exceeding 40 million lines of code.
The infrastructure gap isn’t computational power—it’s organizational memory. While large language models excel at code generation, they struggle to maintain context across complex, interconnected systems where critical knowledge lives in senior engineers’ heads and context spans dozens of tools and millions of lines of legacy code.
The Enterprise Context Crisis
Modern software systems weren’t designed for AI agent operation. Enterprise engineering teams face a fundamental mismatch between how AI agents access information and how complex software systems actually work. Traditional approaches rely on stateless retrieval or short-term context windows, methods that break down when workflows become business-critical and long-running.
The bottleneck manifests in production reality: agents can generate individual functions but cannot safely modify interconnected systems, debug cross-service failures, or understand architectural intent across large codebases. This creates what industry practitioners call the “demo-to-production gap”—agents that work impressively in isolated scenarios but fail when deployed against real enterprise complexity.
Potpie addresses this through ontology-first architecture that transforms unstructured engineering data into persistent, structured memory layers built on knowledge graphs and semantic representations. The platform unifies context from source code, tickets, logs, documentation, and reviews, creating searchable, tagged indexes that allow agents to operate with the same system-level understanding as experienced engineers.
Foundational Architecture for Agent-Native Engineering
The company’s approach centers on spec-driven development where specifications become the source of truth rather than existing code. AI agents plan features end-to-end first, mapping dependencies and edge cases before writing implementation code. This architectural shift enables agents to reason across massive, interconnected systems rather than generating isolated code fragments.
Potpie automatically generates structured behavior definitions for each AI agent, outlining how they should operate within specific codebases. The platform builds graphical representations of software systems, infers behavior patterns across modules, and creates structured artifacts that enable consistent, safe agent operation. Rather than acting as another coding assistant, Potpie creates the foundational context layer that makes enterprise-grade agent deployment possible.
The platform actively creates context as systems evolve—updating documentation when pull requests are created, generating system designs when tickets are opened, and maintaining current understanding of system architecture and dependencies. This dynamic context management addresses the reality that enterprise systems constantly change while maintaining the contextual understanding agents need for safe operation.
Enterprise Validation at Scale
Early enterprise deployments demonstrate the production impact of foundational context infrastructure. One customer with a codebase exceeding 40 million lines reduced root cause analysis for production issues from nearly a week to approximately 30 minutes, with engineers acting as reviewers instead of investigators. Another enterprise customer maintaining decades-old systems used Potpie to update and generate tests automatically, compressing work that previously required multiple sprints into much shorter cycles.
Potpie currently serves Fortune 500 and publicly listed companies in regulated industries, including healthcare and insurtech sectors where agent reliability requirements are stringent. The platform’s open-source projects have surpassed 5,000 stars on GitHub, creating organic adoption that drives enterprise interest and validation.
More than 70 companies are running Potpie in live production environments, particularly in knowledge-intensive and regulated domains where traditional agent approaches fail due to context and reliability requirements. This production adoption validates the market need for foundational agent infrastructure that can handle enterprise-scale complexity.
Infrastructure Consolidation Movement
The $2.2 million pre-seed round was led by Emergent Ventures with participation from All In Capital, DeVC, and Point One Capital. The funding will support early enterprise deployments, expand the engineering team, and continue building Potpie’s core context and agent infrastructure.
Anupam Rastogi, Managing Partner at Emergent Ventures, noted the architectural significance: “In large enterprises, the real challenge is not generating code, it is understanding the system deeply enough to change it safely. Potpie’s ontology-first architecture, combined with rigorous context curation and spec-driven development, creates a structured model of the entire engineering ecosystem.”
The funding reflects broader infrastructure consolidation trends as enterprises move from AI experimentation to production deployment. Traditional cloud platforms optimized for request-response patterns struggle with AI agent execution requirements for long-running, stateful processes with complex context dependencies.
Founded by Aditi Kothari and Dhiren Mathur in October 2023, Potpie addresses the fundamental shift from prototype AI tools to enterprise-grade agent infrastructure. The founders spent nearly two years building the foundational layer that understands codebases and creates underlying knowledge graphs before launching publicly in January 2025.
Looking Forward
The next 12-18 months will determine whether specialized context infrastructure becomes standard for enterprise agent deployment. Early production results suggest that agent-native platforms like Potpie may be necessary for organizations to safely deploy AI agents across complex software systems.
As Kothari emphasized, “AI readiness is not about picking the right model. It’s about building systems that can support intelligence over time.” The infrastructure layer enabling agents to operate with enterprise-grade reliability and contextual understanding represents a foundational requirement for the broader agent deployment trend across Fortune 500 organizations.
Enterprises evaluating AI agent infrastructure can explore Potpie’s approach to context management and agent deployment challenges. For organizations managing complex software systems, Overclock provides complementary orchestration capabilities that help coordinate multiple AI agents and automate complex workflows across enterprise environments.