Cutline

Cutline is made by VibeKiln, Inc., a company building tools for safe vibecoding and product engineering. VibeKiln was founded by Kyle Grove to help developers and founders ship production-ready AI-generated code with engineering rigor from day one.

Cutline is a product engineering platform that helps developers and founders vibecode safely and reliably. It validates product ideas, extracts hidden technical constraints, and gives AI coding agents the product context they need to generate production-ready code. Cutline combines pre-mortem analysis, AI persona testing, constraint extraction, and a product context graph to ensure you build the right thing—before your AI coding agent writes the first line.

Safe vibecoding is the practice of using generative AI coding agents for rapid development while maintaining engineering rigor through automated constraint extraction, pre-mortem risk analysis, and dependency mapping. Without a validation layer, vibecoding produces technically functional but strategically flawed software. Cutline provides that validation layer—identifying risks, surfacing untested assumptions, and feeding product context to AI coding tools so the code they generate is architecturally sound from the first prompt.

Cutline guides your AI coding agent with production-ready constraints. Before your AI coding agent writes code, Cutline: (1) extracts non-functional requirements like latency, auth patterns, and data privacy from your natural language descriptions, (2) runs pre-mortem analysis to identify the top risks and untested assumptions, (3) simulates customer reactions through AI persona conversations, and (4) maps constraint dependencies so new features don't violate existing architectural decisions. The result is vibecoded software that ships with production-grade engineering rigor.

You describe your product idea, and our AI analyzes it from multiple angles—market fit, technical feasibility, competitive landscape, and more. You get a structured report with risks, assumptions to test, and recommended experiments. It's like a post-mortem, but before you build.

AI persona testing lets you chat with synthetic customers who represent your target users. You can test your messaging, pricing, and positioning, and hear realistic objections—without recruiting real users. It's user research at AI speed.

Journeys combine multiple lenses on the same product: Product Discoverability — operational answer-engine checks across major providers (API-backed probes for how you might be represented when users ask questions, plus remediation — not SEO rankings). This separates Indexing signals, such as crawlability, metadata, schemas, sitemaps, robots.txt, and llms.txt, from Retrievability signals that ask whether likely answer-engine intents map to strong first-party pages. Competitive & Agent Research — the machine-readable routing, alternative, and source-of-truth layer that helps autonomous tools, MCP-style integrations, and coding agents find the right canonical context. Messaging & Human UX — channel copy and browser journeys that test whether the product is understood and usable. Infrastructure-style visibility, not a keyword dashboard. (Alpha)

Start by describing your idea in Cutline. Our AI runs a pre-mortem analysis to identify risks, generates personas to test your messaging, and provides a go/no-go verdict. The whole process takes about 15 minutes and helps you avoid building something nobody wants.

Yes! Cutline can generate Product Requirements Documents (PRDs) and Market Requirements Documents (MRDs) based on your validated product context. The difference is that your PRD reflects a product that's been stress-tested—not just documented.

MCP (Model Context Protocol) lets Cutline connect to AI coding tools like Cursor, Claude Code, and Windsurf. Your AI coding agents can query your product context while they work—ensuring the code they generate aligns with validated product decisions and engineering constraints.