decior is built for AI-native teams where product managers (not engineers) are now the bottleneck. Unlike tools that stop at insights or PRDs, it turns raw discovery inputs (interviews, usage data) into evidence-backed feature proposals with UI changes, data model implications, and dev-ready tasks. It’s designed for coding agents like Cursor, not just human readers.
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Maker
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Hey everyone! Feally excited and slightly nervous to share this.
This started from a simple frustration: engineers can now ship incredibly fast with tools like Cursor, but as a result, product managers have quietly become the bottleneck. The slow part isn’t building anymore. It’s figuring out what to build and turning messy customer insights into something actionable.
I kept seeing the same pattern: interviews, notes, analytics dashboards… then hours or even days stitching it all into a spec. It felt like a workflow that should be compressible.
So I started experimenting with a different approach: what if you could take raw discovery inputs (transcripts, usage data, notes) and go straight to an evidence-backed feature proposal with dev-ready tasks?
Early versions were honestly pretty rough... especially around trust. The biggest shift was focusing on grounding everything in real evidence (quotes, sources) so that product managers can verify where every insight comes from.
Still very early, but would genuinely love feedback. Especially how you’re currently going from “we talked to users” to “here’s what we’re building next.”