Inspiration
Both freelancers and small companies waste time each week doing the same unpaid job - trying to interpret the ambiguous client brief, asking questions, creating the scope document and sending a proposal - only to find that the client has disappeared or changed their mind. Scope is work, but very rarely is it paid work.
I did not set out to create yet another AI chatbot. What I aimed to do was to develop something that a freelancer would actually use on Monday morning before meeting the client. ScopePilot was born out of that. How could the ambiguous brief be turned into a professional scope in just one minute?
What it does
Scopely transforms a disorganized client brief into an organized and editable project scope.
Copy the brief or "Try sample brief," and the AI will create 13 sections in total: project overview, objectives, target audience, assumptions, missing information, in scope, not in scope, deliverables, milestones, time frame, cost estimate range, risks, and next steps.
Every single section can be edited right away. You have an option to copy the entire scope as Markdown or download it, re-generate it, and save it. Saved scopes can be found under "Saved" along with the exact date and the Open button. The whole process takes less than a minute.
Among all sections, the "missing information" is the one that attracts users the most. Unlike guessing, the AI provides the freelancer with everything he/she needs to know from the client before beginning work.
How was it built
ScopePilot was built from end to end using Lovable, a platform for building AI apps which translates natural language prompts into a fully-stacked React application.
Frontend: React + Vite + Tailwind CSS. Three pages: Home, Scopes, Saved.
Backend: A server-side API endpoint is used for making the call to the LLM. The API key is kept as an environment variable and does not get exposed to the client side.
AI: One call to the LLM with a precise system prompt that outputs a response as valid JSON with exactly thirteen fields. No need for fine-tuning, no need for multi-agent logic.
Storage: Supabase table projects(id, title, raw_brief, scope_json, created_at).
Source code: The code gets synced to a public Github repo by Lovable.
I deliberately picked the tiniest architecture possible which could still show a working product. Not a single thing extra: no authentication, no payments, no administration.
Challenges
Avoiding "AI wrapper": This was a part of hackathon challenge requirements. I managed to bypass this trap by treating the AI response not as a text but as structured data. Thirteen fields are the result, the workflow around them makes ScopePilot useful.
Reliable response: To get the model returning clean JSON for thirteen fields and not inventing budgets and dates which were not mentioned in the brief, we had to iterate over the system prompt and validate the AI output in a simple text box before developing any user interface around that.
Constraints of non-coder building: I considered ScopePilot development from the point of view of a person who doesn't write code and uses AI app builders. It means all decisions should be implementable in the tool and GitHub.
Accomplishments to be proud of
A working, live product, not a pitch deck. All built and shipped inside the hackathon window.
A thirteen-section structured output from an ambiguous input, generated in seconds.
Editable inline output, Markdown copy, download, regenerate, save, and reopen, in simple English the full loop works.
Server-side AI calls with no key exposed to the client.
A public GitHub repository with setup instructions and environment variable documentation.
What I learned
The primary risk in AI products lies in the inaccurate responses to the noisy inputs, not in the user interface. Verifying the prompts, using JSON format where possible, and being more specific with the plans. Even non-programmers can develop full-stack AI applications, provided that they think as engineers. Starting with the testing, which is the most critical functionality and eliminating anything else from the scope.
What's next for ScopePilot
Export formats: PDF and Google Docs export for client-facing proposals.
Shareable client links: send a read-only scope URL to the client for approval.
Template library: pre-built scope templates by industry such as web design, branding, content, development.
Team collaboration: shared workspaces for small agencies scoping together.
Integrations: push scopes directly into Notion, Linear, or Asana as project tasks.
Pricing signal: a free tier for solo freelancers and a paid tier for agencies.
The goal is simple, make ScopePilot the first tab a freelancer opens when a new brief lands in their inbox.
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