Hey Product Hunt! 👋 I’m Evgenii, the maker of Intentra. I built Intentra because I kept explaining the same product context to coding agents: what we’re building, why a requirement exists, what a domain term means, and which decisions we’ve already made. That understanding needed a place to live beyond the current conversation. I love domain-driven design and clean architecture, especially the emphasis on shared language and explicit business rules. I was also inspired by [OpenSpec](https://github.com/Fission-AI/Op...) and [Matt Pocock’s grill-with-docs workflow](https://github.com/mattpocock/sk...): ask the difficult questions, clarify assumptions, and preserve the reasoning. Those ideas shaped Intentra into an AI product analyst. You discuss your product with it; it asks questions, checks what you say against existing knowledge, and proposes linked requirements, rules, terms, and decisions. You review and approve new knowledge before it becomes shared context. The part I care about most is what happens after that first specification. Products change. A business rule changes, a requirement loses its justification, or an old decision stops making sense. Intentra preserves the history and flags dependent knowledge for review. Its analysis also looks for gaps and contradictions. Your team can read the approved knowledge as a Product Passport, while Claude Code, Codex, and Cursor can retrieve context for their tasks through MCP. My goal is to keep the “what” and “why” useful throughout a product’s life, so every new coding session has something solid to build on. I’d love to hear how you handle this today: what product context do you find yourself explaining to your coding agent over and over?

Intentra

Your AI analyst for living product requirements

Your AI analyst for living product requirements