LaunchMap AI

LaunchMap AI is a client launch-readiness workspace for small agencies, freelancers, implementation teams and startup studios. Talal Khawaja and Aqeela Urooj built it for the AI Factory (native.builder) Hackathon on lablab.ai, which ran from 3 to 10 August 2026. The application was created primarily in native.builder. Our local work was planning, QA, evidence, documentation, source validation and submission preparation.

Problem

Client launches rarely start with a complete brief. The website copy isn't final, nobody has named the approver, one integration is "probably fine", and the date was set before any of that was known. Those gaps don't disappear. They come back as delay, rework and awkward handoffs. We wanted a tool that makes them visible on day one, in a form a team can act on.

How it works

The user starts an assessment, or loads a synthetic café sample, and enters the company, website, project type, launch date, written brief and the channels and integrations they need. LaunchMap then runs a deterministic analysis of the brief alone and returns seven result sections:

• a readiness score with its evidence;
• missing information and assets;
• dependencies and blockers;
• a risk register;
• required approvals;
• follow-up questions for the client;
• a prioritised action plan and an editable launch checklist.

Owners, priorities, dates, checklist items and questions can all be edited. When the user has reviewed every section, they can explicitly approve the plan, copy its summary or print a four-page report.

native.builder's Product Architect scoped the requirements, its builder agent generated and iterated the Vite and React application, and native.builder publishes it to a public URL. GitHub Sync exports the generated source to a public repository. The README records that the local production build of that source matches the public deployment's primary bundle byte for byte.

Key decisions and trade-offs

• Deterministic scoring. The number comes from code, with visible deductions per category. Our responsible-AI notes call for qualitative confidence rather than false precision.
• Honest about what's off. Public-website enrichment through Bright Data was planned, but in this release it is marked unavailable and deferred, and it is not claimed as active. A failed or missing source should lower confidence, never produce invented facts.
• "Approved" means reviewed. Approval means a person reviewed the workspace output. It doesn't mean a client signed off on a launch. The app recommends and organises work. It never approves launches, signs contracts, buys services, changes client systems or sends messages.
• No-code origin, stated plainly. The product came from native.builder, and the write-up doesn't present it as hand-built.

What's next

Our architecture notes describe the next layer: bounded public-website retrieval with source URLs and timestamps, and server-side persistence with secrets kept out of the browser. Both would be added behind the same review-first boundary.

Small agencies, freelancers and implementation teams often start a client launch with an incomplete brief, missing assets, unclear integrations and vague approval requirements. The gaps surface late, as delays, rework and missed dependencies.

A guided assessment takes the company, project type, launch date, brief and the required channels and integrations. A deterministic, brief-only analysis produces an explainable readiness score with evidence, gaps, dependencies, risks, approvals, blockers, follow-up questions and a prioritised checklist. A person reviews and edits every section, then approves, copies or prints the plan.