San Francisco, California: Potpie AI has raised $2.2 million in a pre-seed funding round to make AI agents usable inside real-world engineering systems, addressing one of the most persistent challenges in enterprise software development – context fragmentation across large-scale codebases.
Based in San Francisco, California, Potpie AI is building a foundational context layer that allows AI agents to operate across complex, large-scale codebases the way experienced engineers do.
As software teams move faster than ever, the systems they build and maintain were not designed for AI agents to function within them.
Codebases often span millions of lines, context is scattered across multiple tools, and critical system knowledge frequently resides with a handful of senior engineers. Potpie AI aims to change that dynamic by unifying context across the entire engineering stack.
The $2.2 million round was led by Emergent Ventures, with participation from All In Capital, DeVC, and Point One Capital.
The capital raised by Potpie AI will be deployed to support early enterprise deployments, expand its engineering team, and continue building its core context and agent infrastructure.
Also Read: AI Travel Planning Gets a Boost as MakeMyTrip Collaborates with OpenAI
Potpie AI: Addressing the Context Gap in Generative AI
As generative AI adoption accelerates, most tools focus primarily on surface-level code generation. However, without access to system-level understanding, tooling history, and architectural intent, large language models struggle in real production environments.
Traditional approaches depend heavily on senior engineers to manually stitch together system context – an approach that does not scale effectively, particularly when AI agents are introduced.
Potpie AI addresses this by unifying context across the entire engineering stack and enabling spec-driven development. The platform pulls in information from source code, tickets, logs, documentation, and reviews, linking it together and making it accessible to AI agents.
With Potpie AI, the specification becomes the source of truth. Agents first convert requirements into a clear implementation plan, mapping dependencies and edge cases, and aligning tests and rollout steps before writing any code. The company’s principle is straightforward: an agent is only as effective as the information it can access and the tools it can use.
“As AI makes code generation easier, the real challenge shifts to reasoning across massive, interconnected systems. Potpie is our answer to that shift, an ontology-first layer that helps enterprises truly understand and manage their software,” said Aditi Kothari, CEO and co-founder of Potpie.
Enabling Enterprise-Grade Automation
Potpie AI enables teams to automate high-impact and non-trivial use cases across the software development lifecycle. These include debugging cross-service failures, maintaining and writing end-to-end tests, blast radius detection, and system design.
The platform is built for enterprise companies with large and complex codebases, starting at around one million lines of code and scaling to hundreds of millions.
Rather than acting as a conventional coding assistant, Potpie AI builds a graphical representation of software systems, infers behavior and patterns across modules, and generates structured artifacts that allow AI agents to operate consistently and safely.
The platform also continuously creates and updates context as systems evolve. When pull requests are created, Potpie AI can automatically update documentation and tickets.
When tickets are opened, it can generate system designs. It generates structured behavior definitions for each AI agent, outlining operational boundaries within a specific codebase.
Simultaneously, it builds a searchable, tagged index across APIs, services, databases, and components, narrowing the search space and improving reliability.
Also Read: JioHotstar and OpenAI Launch ChatGPT-Powered Conversational Streaming Experience in India
Founded During the First Wave of Generative AI
Potpie AI was founded by Aditi Kothari and Dhiren Mathur, who began working on the problem in October 2023, during the first wave of generative AI adoption.
While much of the industry focused on knowledge workers, the founders identified a fundamentally different challenge for developers. Code is non-linear, deeply interconnected, and distributed across large systems.
The team spent nearly two years building the foundational layer that understands codebases and creates the underlying knowledge graph before publicly launching Potpie AI in January 2025.
Early Enterprise Deployments
Early deployments highlight the scale of the problem Potpie AI is solving. One customer with a codebase exceeding 40 million lines reduced root cause analysis time for production issues from nearly a week to approximately 30 minutes, with engineers transitioning from investigators to reviewers.
Another customer managing decades-old systems used Potpie AI to update and generate tests in the background, compressing work that previously required multiple sprints into a significantly shorter cycle.
Anupam Rastogi, Managing Partner at Emergent Ventures, commented: “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.
This allows AI agents to reason across services, dependencies, tickets, and production signals with the clarity of a senior engineer. That is what makes Potpie uniquely capable of solving complex RCA, impact analysis, and high-risk feature work even in codebases exceeding 50 million lines.”
Enterprise Focus and Open-Source Momentum
Potpie AI currently works with Fortune 500 and publicly listed companies in regulated industries, including healthcare and insurtech. Its open-source projects have surpassed 5,000 stars on GitHub, strengthening enterprise adoption momentum.
“AI readiness is not about picking the right model,” Aditi Kothari added. “It’s about building systems that can support intelligence over time. Our goal is to make Potpie the foundational layer engineering teams rely on to build, operate, and evolve complex software with AI built in from the start.”
