Potpie AI Secures $2.2M to Bring Smarter AI to Complex Engineering Workflows

Software development is evolving rapidly, but the environments developers work in haven’t always kept pace. Today, Potpie AI — a startup focused on making AI agents genuinely useful inside real-world engineering systems — announced it has raised $2.2 million in pre-seed funding. The round was led by Emergent Ventures with participation from All In Capital, DeVC, and Point One Capital. The capital will support early enterprise deployments, expand the engineering team, and further develop Potpie’s core context and agent infrastructure.

Tackling the Context Problem in Engineering Systems

Generative AI tools have made impressive strides in producing working code, but they often struggle inside complex, interdependent systems where context is critical. Large language models can generate code, yet without deep insight into system-level architecture, tooling histories, and cross-service dependencies, their effectiveness in production environments is limited.

Traditionally, senior engineers hold much of this contextual knowledge together. As systems scale into the tens of millions of lines of code, that model becomes fragile. Potpie was designed to address this gap by building a foundational context layer that unifies information across source code, tickets, logs, documentation, and reviews. By linking these elements into a structured representation of the codebase, the platform enables AI agents to reason about systems in a more holistic way.

From Specs to Execution With Greater Confidence

A core principle behind Potpie is treating the specification — not the existing code — as the source of truth. Before generating any code, agents translate requirements into a clear implementation plan, map dependencies and edge cases, and align testing and rollout strategies.

Rather than functioning as another coding assistant focused on autocomplete, Potpie constructs a graphical model of the software system. It infers behavior across modules and produces structured artifacts that guide how agents operate. This includes generating Agent.md files to define agent behavior within a codebase, as well as building searchable indexes across APIs, services, databases, and components to reduce ambiguity and improve reliability.

The platform also evolves alongside the system. When pull requests are created, it can update documentation and tickets. When tickets are opened, it can generate system designs. When releases ship, it can produce release notes automatically. The aim is to continuously curate and maintain context instead of letting it fragment over time.

Built for Enterprise-Scale Codebases

Founded by Aditi Kothari and Dhiren Mathur, Potpie emerged during the early wave of generative AI adoption in late 2023. While much of the industry focused on AI for knowledge workers, the founders identified a distinct challenge facing developers: codebases are non-linear, deeply interconnected, and distributed across multiple tools and teams.

The company spent nearly two years developing its ontology-first architecture and knowledge graph before launching publicly in January 2025. The platform is designed for enterprise environments, starting at codebases of roughly one million lines and scaling to hundreds of millions.

Early Results Reflect the Scale of the Problem

Initial deployments underscore the complexity Potpie aims to manage. In one case, a customer with more than 40 million lines of code reduced root cause analysis for production issues from nearly a week to around 30 minutes, with engineers shifting into reviewer roles rather than investigators. Another enterprise maintaining decades-old, hardware-integrated systems used the platform to generate and update end-to-end tests in the background, compressing work that previously spanned multiple sprints into a significantly shorter cycle.

Potpie 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, contributing to enterprise interest.

A Shift in How AI Fits Into Engineering

As AI-driven code generation becomes more accessible, the harder challenge is enabling AI to reason across entire systems safely. Potpie’s structured, context-first approach reflects a broader shift in the developer tooling landscape — one that emphasizes system-level understanding rather than surface-level automation.

The company’s latest funding round signals growing investor attention toward infrastructure that makes AI agents practical in real-world engineering environments. If successful, context — not just generation — may prove to be the defining layer that allows AI to move from assistant to integrated participant in complex software ecosystems.