Stage

Product Introduction

1. Definition: Stage is a collaborative prototype deployment and review platform. Technically, it is a cloud-based service that ingests design and code artifacts from tools like Figma, Cursor, Claude, or GitHub, automatically generates a live, shareable web application (prototype), and layers on a contextual feedback and workflow system.
2. Core Value Proposition: Stage exists to bridge the costly gap between design/prototyping and engineering implementation. Its primary value is accelerating product development cycles by transforming static designs or AI-generated code into a shareable prototype with one link, enabling precise, in-context collaboration and leveraging AI (Claude) to iterate on feedback before filing formal engineering tickets in Linear or Jira.

Main Features

1. One-Click Prototype Publishing: Stage automatically builds and hosts a live, interactive web preview from source files. How it works: Users connect their Figma file, GitHub repository, or paste code from AI assistants (Claude, Cursor). Stage's backend infrastructure processes these inputs, builds a static or dynamic preview using standard web technologies (HTML, CSS, JavaScript), and deploys it to a globally accessible, unique URL on its CDN.
2. Contextual Pin-Based Commenting: This feature allows for granular feedback directly on the live prototype. How it works: Reviewers (designers, PMs, engineers) can click on any UI element within the hosted prototype to "pin" a comment. This comment is stored with precise coordinates and element metadata, creating a visual feedback layer over the prototype that all stakeholders can see and reply to, eliminating vague references.
3. AI-Powered Iteration (Claude Integration): Stage integrates Anthropic's Claude AI to act on feedback automatically. How it works: Once comments are pinned, a user can prompt Stage to "fix" noted issues. Stage sends the prototype's source code and the specific comments to Claude via API, which then generates corrected code. This creates "v2" of the prototype, allowing for rapid iteration before human developers write production code.
4. Approval Workflow & Ticket Creation: The platform formalizes the sign-off process and handoff. How it works: Designers and product managers can mark the prototype as "approved." Once final sign-off is achieved, Stage can automatically generate and file a well-structured ticket in connected project management tools like Linear or Jira, containing the prototype link, all pinned comments, and the final approved version for engineering implementation.

Problems Solved

1. Pain Point: It eliminates the inefficient, error-prone feedback loop where stakeholders use separate tools (email, Slack, screenshot markups) to discuss static mockups or early builds, leading to misinterpretation and rework.
2. Target Audience: Product Designers (seeking accurate feedback on interactions), Product Managers (needing to validate UX flow before development), Frontend Engineers (receiving clear, contextual requirements), and Startup Founders/Small Teams (requiring fast iteration with minimal overhead).
3. Use Cases: 1) Design Handoff: A designer publishes a Figma prototype to Stage for PM and engineering review, collecting precise feedback. 2) AI Code Review: A developer pastes Claude-generated frontend code into Stage for the team to test and comment on functionality. 3) Stakeholder Demos: A PM shares a single Stage link with executives or clients for interactive demos and sign-off, bypassing complex setup.

Unique Advantages

1. Strengths & Limitations (Pros & Cons):

   • Pros: Dramatically reduces time-to-feedback by providing a live, central source of truth. The AI iteration feature is a unique differentiator for rapid prototyping. It reduces friction in the handoff process by automating ticket creation. The "one link" model is incredibly simple for reviewers.
   • Cons: The prototype is a preview environment; it does not handle complex backend logic or databases. The AI-generated fixes may require developer review for production-quality code. Value is maximized for teams already using Figma, Claude, and Linear/Jira; adoption may be lower for teams on other stacks.

2. Key Alternatives & Differentiation:

   • Figma Comments & Dev Mode: Figma's native commenting is for static designs, not live code. Dev Mode helps inspect specs but doesn't create a working, shareable web app. Stage complements Figma by adding the interactive, hosted layer.
   • Vercel/Netlify for Previews: These platforms automatically deploy GitHub branches to URLs. However, they lack built-in, contextual commenting tools and approval workflows tailored for non-technical stakeholder review. Stage adds the collaborative layer on top of deployment.
   • Standalone Bug Trackers (Usersnap, Marker.io): These tools capture feedback on live websites. Stage differentiates by focusing on the pre-production prototype phase and integrating the source tools (Figma, AI code) directly, creating a closed loop from design to ticketed task.

Frequently Asked Questions (FAQ)

1. How does Stage create a prototype from Figma? Stage uses the Figma API to extract design data, translates vector and component information into HTML/CSS, and generates an interactive web preview that simulates the intended user interface and flow, hosted on its own infrastructure.
2. Is Stage a replacement for developer handoff tools like Zeplin? Stage can serve a similar handoff function but with a key difference: it provides a working interactive prototype instead of just static specs and assets. It is better suited for validating interaction and flow before final development begins.
3. Can I use Stage with code from GitHub repositories? Yes, Stage connects to GitHub to automatically build and deploy branches or specific commits as shareable prototype links, enabling code-based reviews alongside design-based ones.
4. How accurate are the AI-powered fixes from Claude? The AI fixes are effective for straightforward UI/UX adjustments, copy changes, or minor styling issues. For complex logic or architectural changes, the AI-generated "v2" should be treated as a suggested starting point for a developer.
5. What happens to the prototype link after the project is done? Prototype links are persistent but access can be managed. Teams can archive projects. It's advisable to use Stage for iterative review phases, with final code living in the main code repository and deployed via standard hosting services.