NewNew in potpiePotpie selected among 20 for Google’s 2026 Accelerator

AI native SDLC automation for
large scale engineering.

We embed engineers in your org, capture how your systems actually work into a living context graph, and run AI agents on top of it, inside your VPC, on your infrastructure.

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Secure, faster, smarter.
All on your own stack.

We are trusted by 200+ developers at leading engineering organisations for context engineering, automated debugging and spec generation.

Ranked #1

SWE-bench Verified

5.7k+ stars

We are open source

“Our context was scattered across tools. Potpie brought everything together, sped up debugging and development, and helped our team ship faster.”

Rakesh Yadav

Founder and CEO, FloBiz

Built around your people

Your people.
Powered by your context.

From architecture decisions to regression tests, give every engineer agents that understand how your company works.

Why did checkout latency regress after the last release?

checkout-service

Root-cause analysis

Inspect recent code changes
Trace the request path
Connect incidents and past decisions
Propose a fix with supporting sources

Ready for human review

Write regression tests for this retry-policy change.

Retry policy

Change to verified fix

Read the policy and implementation
Map downstream behavior
Draft targeted regression tests
Prepare the changes for human review

Ready for human review

Staff Engineer

Less explaining. More doing.

Enterprise AI isn’t about having the best tools. Its about making them work together.

This is how Potpie works at the architecture level. Layers that turn the tools, agents and models you already have into an AI-native SDLC for your organisation.

User Interfaces

IDE
Visual Studio

Github
Copilot

Claude
Code

Internal Tools /
Dashboards

Agents Layer

Specialized agents execute tasks

Orchestrator Agent

Plans, delegates and monitors execution

Debugging
Agent

Impact Analysis
Agent

Code Gen
Agent

QA / Testing
Agent

Custom / Special
Agents

Harness Layer

The control plane that makes agents reliable

Orchestrations
& Task Routing

Context Selections
& Compression

Tool Executions
& Sandboxing

Memory & State
Management

Feedback Loops
& Validation

Observalibilty
& Guardrails

Context Layer

Enterprise context that powers intelligence

Codebase

Services, APIs, dependencies, architecture, commits

Knowledge Base

Docs, PLM, wikis, standards, SOPs, design docs

Jira / Tickets

Bugs, features, tasks, requirements, history

Code Graph

AST, call graph, data flow, dependencies

Knowledge Graph

Entities, relations, semantics

Ontology Layer

Domain concepts, mapping, relationships

Model Layer

Interchangeable intelligence

Claude

OpenAI/ GPT

Gemini

xAI

Other & Future
Models

Connected from the first spec to the final review.

Enterprise scale

Built for your entire enterprise context.

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One living context layer.
Across teams and repositories.

Every source. One context graph.

Use any
model.

Your subscriptions.
Or your own weights.

Real-time context creation.

Open source.
Self-host, contribute, and build.

Explore the repository

Your infrastructure.
Your control.

Deploy in your VPC, through approved APIs.

Every run,
accounted for.

Agent activityExample

Impact analysisReviewed

Test generationPassed

Code reviewApproved

Ask your codebase anything.

Grounded answers. Sources you can follow.

Context workspace

potpiecheckout-service main

Why did checkout latency regress?

A recent PR shortened the gateway timeout from 30s to 5s. Slow calls now retry instead of completing.

↗ PR #11↗ ENG-1204↗ timeouts.md

Retry policy updateSpec draft

01 Connect

Connect your engineering stack

GitHub, GitLab, Jira, Linear, Confluence, PLM and infra — connected through approved APIs.

Forward-deployed engineers embed from week one.
Custom connectors and parsers for your stack.
Runs inside your VPC, read-only by default.

GitHub

Jira

Confluence

GitLab

Sentry

Linear

Teams

Databases

and more

Approved APIs

Your engineering workspace

A connected foundationContext from the tools you already use

GitHubCode & pull requests

Connected

JiraTickets & decisions

Connected

ConfluenceDocs & knowledge

Connected

Inside your VPCRead-only by default

02 Understand

Map how everything relates

Services to files, tickets to decisions, engineers to what they own. Context connects across systems.

Mapped with your teams, not from outside.
Decisions, ownership and dependencies, connected.

Context explorer

payments-service ↵

⌘

Servicepayments-service

Context

lives in

src/payments/

explained by

Retry policy

tracked in

ENG-204

owned by

Payments team

03 Build intelligence

The graph becomes an ontology

Entities, relations and domain concepts become a typed model of your engineering, rebuilt on every commit.

A typed ontology of your domain concepts.
Refreshed with every commit and ticket.

Engineering ontology

Domain modelRelationships

Entity / ServicePaymentService

ownsPaymentEntity

depends_onLedgerService

governed_byRetryPolicyConcept

Updated with your code, not left behind.

04 Deploy workflows

Agents start doing the work

Debugging, spec generation, testing and impact analysis run on the graph, inside your existing tools.

Specs and tests generated with full context.
Root-cause and impact analysis before you merge.

What breaks if we change the retry policy?

Impact analysis agent

A plan, grounded in your code.2 / 3

✓
Trace affected servicesDependencies and owners, located
✓
Read related decisionsSpecs, tickets and past changes
Prepare an impact reportFindings and tests for your review

05 Improve

Tuned by FDEs, with your team

Forward-deployed engineers refine the harness with every run. The system compounds and the context stays yours.

Your team’s feedback sharpens every run.
Context, harness and graph stay yours.

Built with your team

YouFDE

One team. A tighter loop.Your engineers + Potpie FDEs

Your team · review

“Include the downstream billing tests in the impact report.”

Potpie FDE · refine

Harness updated. That context is part of the next run.

✓

Context infrastructure for large organizations.

Potpie brings code aware automation to every part of the team. From juniors to staff engineers, everyone ships faster when the codebase finally makes sense.

90%

Reduction in onboarding time for new engineers

80%

Repetitive engineering tasks automated end-to-end

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8×

Token savings

Own the intelligence, on any model your team prefers.

Codex, Cursor, Claude Code and Copilot are strong agents with thin context. Potpie becomes the layer underneath, with the engineers who make them work in a large org. Deployed in your VPC, owned by you.

Context layer

Potpie

A living graph of the whole org: code, tickets, docs, architecture and decisions, rebuilt on every commit

CODEX

Repo index + session memory

CLAUDE CODE

Repo + CLAUDE.md + memory

CURSOR

Repo index, rules + memory

COPILOT

Repo index + Spaces

Context scope

Potpie

One graph, shared by every team, tool and agent

CODEX

Per workspace

CLAUDE CODE

Per repo, per machine

CURSOR

Per workspace

COPILOT

Per GitHub workspace

Deployment & data

Potpie

Your VPC or on-prem so you own the context layer

CODEX

Local + provider cloud

CLAUDE CODE

Local + provider API

CURSOR

Provider cloud, some local

COPILOT

GitHub cloud

Model choice

Potpie

Any model, through your existing subscriptions or ZDR-compliant deployments

CODEX

OpenAI models

CLAUDE CODE

Claude models

CURSOR

Multi-provider

COPILOT

Multi-provider + BYOK

Cost & audit

Potpie

Every run logged with agent, action, tokens and outcome

CODEX

Seat + usage billing

CLAUDE CODE

Usage billing

CURSOR

Seat + usage billing

COPILOT

Seat billing

Set-up & tuning

Potpie

Forward-deployed engineers embed with your team and tune it to your stack

CODEX

Self-serve

CLAUDE CODE

Self-serve

CURSOR

Self-serve

COPILOT

Self-serve

Best for

Potpie

Making all four of these work in a large org

CODEX

OpenAI-native coding

CLAUDE CODE

Terminal-first coding

CURSOR

Editor-first coding

COPILOT

GitHub-native workflows

Context layer

Context scope

Deployment & data

Model choice

Cost & audit

Set-up & tuning

Best for

Potpie

AI-native SDLC automation

A living graph of the whole org: code, tickets, docs, architecture and decisions, rebuilt on every commit

One graph, shared by every team, tool and agent

Your VPC or on-prem so you own the context layer

Any model, through your existing subscriptions or ZDR-compliant deployments

Every run logged with agent, action, tokens and outcome

Forward-deployed engineers embed with your team and tune it to your stack

Making all four of these work in a large org

CODEX

OpenAI

Repo index + session memory

Per workspace

Local + provider cloud

OpenAI models

Seat + usage billing

Self-serve

OpenAI-native coding

CLAUDE CODE

Anthropic

Repo + CLAUDE.md + memory

Per repo, per machine

Local + provider API

Claude models

Usage billing

Self-serve

Terminal-first coding

CURSOR

SpaceX

Repo index, rules + memory

Per workspace

Provider cloud, some local

Multi-provider

Seat + usage billing

Self-serve

Editor-first coding

COPILOT

GitHub

Repo index + Spaces

Per GitHub workspace

GitHub cloud

Multi-provider + BYOK

Seat billing

Self-serve

GitHub-native workflows

Centrally governed AI with enterprise-grade security.

Potpie is built to meet the needs of large organizations with strict compliance and governance requirements.

Book a demo

Trust center

Enterprise-grade security on-prem

Deployed in your VPC or on-prem. No customer data is used to train models.

SOC2 & ISO 27001 compliant

Enterprise-grade compliance certifications to meet your security requirements

Open source, zero vendor lock-in

Self-host it and inspect every line. The graph and workflows are yours to keep.

We don’t sell AI seats. We build your first AI-native team.

You pay for an embedded engagement, not per-seat licences. As the platform carries more of the work, the engagement shrinks and the context stays yours.

Book a demo

Talk with founder

Why we don’t do per-seat pricing

Companies have already spent millions of tokens proving that agents are only as useful as the context they’re given. Pay by the project and actually use the platform.

EMBED

Real work from week one

We embed an AI-first scrum team inside your engineering org to ship real outcomes with agents and not just give people another tool.

UNIFY

All your engineering, one graph

We unify your code, docs, tickets, decisions and workflows into a living context layer, so agents can work with the full picture.

PROVE & REPLICATE

One team first, then entire org

We prove the new way of working with one team first, then help you replicate it across the organization.

Trusted inside Fortune 500 engineering teams

Teams use Potpie to reduce manual effort across core engineering workflows and improve delivery speed.

Read all blogs

Read docs

“Potpie feels like adding two senior engineers to every team. Our workflows improved overnight, and reviews are way more consistent”

Sudarshan Rampuria

Sr. Software Engineer, Astronomer

Engineering

What Does It Actually Mean for an AI Agent to Understand Your Task?

Bharath Kumar

Jul 2, 2026

Engineering

Why Your AI Coding Agent Needs Its Own Sandbox

Shambhavi Shinde

Jul 2, 2026

Engineering

Evaluating AI Coding Agents in the Real World

Kshitij Mishra

May 25, 2026

Compliance

Compliance Is Architecture: Why Your AI Agent Needs a Context Graph

Yash Krishnan

Apr 8, 2026

The people you will actually work with.

Get a glimpse into the team building the future of AI-native work and the culture around it.

Book a call

See open roles

LIFE AT POTPIE

Enterprises shouldn’t have to constantly evaluate AI tools, chase every model release, and carry all the risk when systems fail. At Potpie AI, we believe AI partners should own outcomes, not just sell access to technology. The future belongs to AI companies that own workflows, reduce mistakes, and stay accountable for results. 🚀

EVALS

Benchmarks like HumanEval, MBPP, and SWE-bench helped us understand how coding agents perform. But real engineering work is messier. Enterprise codebases are large, evolving, and full of hidden dependencies. The hard part isn’t just writing code — it’s understanding where the change fits.