Draftlytic vs ChatGPT

ChatGPT is a general-purpose assistant — you can ask it to draft a project plan and get back a reasonable first pass, sitting in your chat conversation as prose. That works fine for a rough sketch. It's a different job to turn that sketch into a structured, editable spec you can actually hand to an AI coding tool: a prioritized feature list, a typed data model, a navigation map with API endpoints, and a non-goals list — as distinct, editable fields, not paragraphs you'd have to reformat yourself.

Draftlytic is purpose-built for that second job. Describe your app idea in a sentence, answer a short set of targeted follow-up questions, and get back a structured project you can reorder, chat-edit, and export as Markdown, PDF, or a Word doc, or push straight to a connected GitHub repo — built specifically for the AI coding tools you're going to use next, like Claude Code, Cursor, or Lovable.

Every section of a Draftlytic project is typed, structured data — features, data model, navigation, constraints — that you can edit, reorder, and re-export any time, instead of a wall of text you'd have to parse and reorganize by hand.

Markdown, PDF, and Word exports, plus a direct GitHub push, are all shaped for handing straight to the tool that's going to build the app — not for reading in a chat window.

Use AI Edit to rewrite any section in plain English — add a feature, change the data model, adjust priorities — and the whole project updates in place, so you're not starting a new conversation from scratch every time requirements shift.

Run an AI Scan on your finished spec and it flags inconsistencies and missing details — an unassigned feature, a data model that doesn't match your navigation map — and suggests specific fixes you can apply in one click.

Every feature gets a priority tier (must-have, nice-to-have, future, or completed), and at Detailed depth each one can carry up to four short, testable acceptance criteria — a concrete definition of done, not just a description.

Entities, fields, and types, alongside a screen-by-screen map of the API endpoints behind each view — the structural decisions an AI coding tool needs before it starts scaffolding, captured once instead of re-explained every session.