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Product Is the New Bottleneck

With AI agents like Cursor, Devin, and Codex accelerating development, engineering teams are moving faster than ever. The bottleneck has shifted upstream: deciding what to build and why it matters. Product teams now struggle to keep up. Requirements are scattered, context is tribal, and documentation is stale. This slows everyone down: people and AI alike.

Engineering Outpacing Product

AI-powered development moves at unprecedented speed. Product definition can’t keep pace, creating a new constraint on delivery.

Fragmented Product Context

Rationale and requirements live across tools and tribal knowledge, lacking structure or a single source of truth.

Stale Documentation

Docs quickly become outdated, lack versioning, and don’t reflect changes, making them unreliable for teams and AI.

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The Cost of Fragmented Context

When product context is scattered and inconsistent, the consequences compound:
  • Misalignment: Teams and AI agents don’t know what matters, leading to wasted effort and conflicting priorities.
  • Duplicated work: Without a source of truth, multiple people solve the same problems in different ways.
  • Missed details: Critical requirements slip through the cracks when context lives in someone’s head.
  • Slower onboarding: New team members and AI agents can’t ramp up quickly without structured, reliable documentation.
  • Regressions and rework: Outdated specs lead to building the wrong thing, or rebuilding the right thing.
  • Lost confidence: Without traceability and versioning, teams and AI can’t trust or act on product information.

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How Product Graph Helps

Product Graph is a software requirements workspace for AI-first teams that captures the what and why in a structured way. It makes structured, machine-readable requirements the canonical source of truth so teams and agents can move from idea to impact with clarity and speed.

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From conversation to clarity

Convert discussions into structured PRDs and clear, LLM-optimized requirements in minutes. The AI assistant drives clarification and feedback, surfacing gaps and mapping key UX flows so teams and agents align before building, not after.

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Agent-ready by default

Turn complex ideas into small, buildable units with goals, context, and acceptance criteria. Your requirements translate directly into executable plans for agents like Cursor, Devin, and Codex.

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Living documentation

AI-assisted updates maintain agent-ready docs as scope and priorities change. Every change is versioned, reviewable, and reversible. No more stale specs causing regressions.

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Connected to your workflow

Pull from docs and wikis, push to planning and issue tracking, and bring requirements into MCP-compatible IDEs and AI clients. Product Graph integrates with Notion, Confluence, Google Drive, Linear, and Jira for a connected source of truth.

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Built for AI-First Teams

Product Graph is designed from the ground up for how modern teams actually work. One source of truth. No more hunting through Slack threads, Notion pages, and Google Docs to piece together what you’re building. Structured requirements replace scattered prose, giving teams and AI agents a single place to look. Optimized for agents. Every artifact is machine-readable and LLM-optimized. Your requirements don’t just document intent; they translate directly into executable context for AI coding agents. Full traceability. Every decision, requirement, and change is connected with complete version history. Know why something was built, when it changed, and who approved it. Roll back when needed. Fits your workflow. Product Graph connects to the tools you already use through robust APIs and protocols like MCP. Pull context in, push requirements out, and keep everything in sync. Clear accountability. Every artifact has an owner. Every change goes through an explicit review path. Whether updates come from humans or agents, the workflow stays auditable and controlled.

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