Userdoc

What it is

Userdoc is a requirements workspace rather than a document editor. You give it rough bullets, screenshots, a Figma file, or an entire source repository, and it expands them into structured artifacts — epics, user stories, acceptance criteria, non-functional requirements, test cases, personas, and user journeys — held in one versioned store with full change tracking. The pitch it leads with is documenting what already exists: connect a legacy codebase and get functional specs for a system nobody on the current team wrote.

Our verdict

Solid, and the most genuinely specification-shaped tool in this category — most AI PRD tools produce a document, Userdoc produces a maintained requirements set with structure you can diff, query, and push downstream. The reverse-engineering angle is the standout: if you have inherited a product with no docs, spending an afternoon here is more valuable than a month of archaeology. The image and Figma context feature is the practical version of the same idea — screenshot the running app, get requirements out.

Where it falls short: there is no free tier at all, and the 14-day trial is capped at one project and ten requirements, which is barely enough to judge whether the structure fits your team. Pricing per seat ($19–$25) is steep next to the $15 generalists, and the structure it imposes is genuinely opinionated — if your team writes narrative PRDs rather than user stories, you will be fighting the tool. The marketing leans hard on enterprise case-study numbers (“6 weeks to 3 hours”) that you should treat as vendor claims, not benchmarks. And its code ingestion produces confident specs for behavior it inferred from code, including bugs — it documents what the system does, which is not always what it should do.

Get value in 10 minutes

1. Start the trial and create one project scoped to a single feature area you actually own — not the whole product, given the 10-requirement trial cap.
2. Skip typing: screenshot three or four screens of your existing app and upload them via Image Context, so the AI works from real UI instead of your description of it.
3. Let it generate stories, then ruthlessly reject the generic ones (“As a user I want to log in”) — keep only stories that name behavior specific to your product.
4. On the two or three keepers, generate acceptance criteria and test cases, then read them as an adversary: any criterion that is not measurable, rewrite it by hand. That edited set is your deliverable — paste it into your tracker and see whether engineers ask fewer questions than usual.