ReviewStage
Stage was created to make human code review faster and more understandable as AI-generated pull requests proliferate. Its hosted product reorganized pull requests into ordered, contextual chapters, while its open-source tools bring the chapters experience to local code and coding-agent workflows.
At a Glance
- Software engineering teams
- Startups and fast-moving technology companies
- Teams adopting AI coding agents
- Organizations using GitHub pull-request workflows
- +1 more
AI Tools by ReviewStage
(2)Luke
Voice Feature Planner for macOS
Stage CLI
AI Code Review CLI Tool
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Latest News
Luke v0.7.1 improves planning-call openings, plan completion, web search, pause handling and plan deletion
Luke v0.7.0 launches the feature-planning direction with named plans, voice calls, sandboxed code reading, web search and GitHub access
Luke v0.6.0 moves its conversational brain and voice to the service, adds Conversation and notebook capabilities, and consolidates provider support around Conductor
Stage CLI repository publishes version 0.1.7
Products & Services
AI-powered GitHub code-review platform that groups pull-request changes into logical chapters, orders them for comprehension, explains what changed, highlights review focus and risk, and syncs comments and approvals with GitHub. The hosted service was scheduled to sunset July 31, 2026.
Free, open-source local tool distributed through npm and an agent-skill installer. It organizes local changes into reviewable chapters, opens a browser UI, supports local diffs and GitHub pull requests, and works with any AI agent.
Open-source macOS 14+ Apple-silicon app for planning features by voice before handing work to a coding agent. Luke reads a selected code folder through sandboxed read-only commands, asks clarifying questions, streams a structured plan, and lets the user copy the plan into Claude Code, Codex, or another agent.
Market Position
Stage positioned itself as a human-centered alternative to conventional GitHub review and automated review bots: rather than only flagging surface-level bugs, it reorganized changes into readable chapters with context and review prompts so humans could understand and approve AI-generated code faster. The open-source CLI extended that differentiated chapter-based workflow to local changes and any coding agent, while Luke addressed the adjacent feature-planning step before implementation.
Leadership
Founders
Charles Pan
Stanford computer science graduate (2022); varsity men's fencing. Previously an early engineer at Yuzu Health and a developer/employee at Five Rings Capital. Cofounder and CEO of Stage.
Dean Stratakos
Stanford computer science graduate (2022); varsity men's tennis. Previously a developer at Five Rings Capital and built coding agents there. Cofounder and CTO of Stage.
Executive Team
Charles Pan
Cofounder and CEO
Stanford CS '22; former early engineer at Yuzu Health and former developer at Five Rings Capital.
Dean Stratakos
Cofounder and CTO
Stanford CS '22; former developer at Five Rings Capital who built coding agents there.
Board of Directors
Founding Story
Charles Pan and Dean Stratakos met in their first Stanford class, later attended classes together, worked at Five Rings Capital, and became roommates in New York. While Pan was an early engineer at Yuzu Health in late 2025, they saw coding agents dramatically increase code production while creating a pull-request review backlog; they started Stage to rebuild code review around how engineers naturally understand changes, like reading chapters of a book rather than an unstructured diff.
Business Model
Revenue Model
The hosted Stage service used paid subscriptions after a free trial; its terms describe recurring subscriptions, and the YC launch offered a free first 14 days. The hosted service was later sunset and subscriptions were canceled. The Stage CLI is free and open source; Luke is distributed as an open-source Apache-2.0 macOS app.
Pricing Tiers
The YC launch post said Stage was free for the first 14 days; it also advertised a launch promotion of 25% off for three months.
Target Markets
- Software engineering teams
- Startups and fast-moving technology companies
- Teams adopting AI coding agents
- Organizations using GitHub pull-request workflows
- Developers using local AI coding agents
- Reviewing AI-generated pull requests
- Reducing large pull-request backlogs and review bottlenecks
- Understanding large or complex diffs before approving or merging
- Giving reviewers a structured way to focus on the most important changes
- Reviewing local uncommitted or branch changes with any AI coding agent
- Planning a feature conversationally before delegating implementation to Claude Code, Codex, or another coding agent
- Yuzu Health
- Legora
- n8n
- Branch