ScopeGuard AI：協助自由工作者保障工作範圍的開放權重 AI 助理

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

A project begins with a clear agreement, a defined price, and a shared understanding of what needs to be delivered.

Then the messages start arriving.

“Could you add one more dashboard?”

“Can we include subscription billing, too?”

“It should be a small change, right?”

For freelancers, these requests can create an uncomfortable choice: absorb the extra work to keep the client happy, or explain why the request may fall outside the original agreement.

The difficult part is not always saying no. It is figuring out what was actually agreed upon, whether the new request fits within that agreement, and how to communicate the answer professionally.

That is the problem I built ScopeGuard AI to help solve.

What I Built

ScopeGuard AI is an AI-powered scope analysis assistant designed for freelancers and independent developers who want to manage client expectations and protect their time.

It compares a client's new request against the original project agreement and helps the freelancer understand whether the requested work appears to be included, potentially out of scope, or in need of clarification.

Instead of returning only a classification, it provides supporting agreement evidence, an explanation, and a recommended next action.

How does it work?

The workflow is straightforward:

1. Provide the original agreement. Enter the agreed project requirements and deliverables.
2. Add the new client request. Describe the additional feature, revision, or change being requested.
3. Analyze the scope. The AI compares the request with the agreement and returns one of three outcomes:
   • Included: The agreement supports treating the request as part of the existing scope.
   • Potentially out of scope: The request appears to go beyond the agreed deliverables.
   • Needs clarification: The available information is insufficient for a confident determination.
4. Review the evidence. Examine the relevant agreement excerpts and the AI's interpretation of them.
5. Take action. Generate a professional client reply or prepare a change-order proposal for additional work.

The change-order generator calculates a proposed fee using the estimated additional hours and the freelancer's hourly rate. The freelancer can then review the proposal before using it with the client.

The goal is not to label every new request as scope creep. Sometimes the work is already included. Sometimes the agreement is ambiguous. Sometimes the client is asking for a legitimate addition that deserves a separate estimate.

The goal is to help freelancers make that distinction before committing to more work.

Who would pay for it?

My initial target customers are freelancers and independent developers who manage projects directly with clients, especially those building websites and software.

For these professionals, unexpected work can affect profitability, delivery schedules, and client relationships. ScopeGuard AI is designed to make scope discussions easier by turning an unclear situation into an evidence-backed analysis and a practical next step.

A potential business model is a freemium product: occasional analyses could be available on a free tier, while paid plans could offer higher usage limits and additional project-management capabilities.

This is a proposed monetization strategy, not a claim that the product already has paying customers. My next goal is to validate whether freelancers find the workflow valuable enough to pay for it.

Demo

Try ScopeGuard AI yourself or watch the video demonstration.

• Live demo — Vercel: https://scope-guard-ai-wine.vercel.app/
• Alternative deployment — Render: https://scopeguard-ai.onrender.com/
• Video demo: https://youtu.be/QE8Jhd25Q34

The application includes a demo scenario involving a business website, additional revisions, an admin dashboard, and subscription billing.

The live application lets you explore the workflow, while the video provides another way to see the project in action.

Code

The source code is available on GitHub:

Explore the ScopeGuard AI repository

The repository contains the application code and setup instructions for developers who want to explore the implementation.

How I Built It

I wanted to build more than a chatbot that answers whether a request sounds like scope creep. The important part was connecting the AI's analysis to the actual agreement and making the output useful for a freelancer's next decision.

1. Open-weight AI at the core

ScopeGuard AI uses OpenAI's gpt-oss-20b open-weight model through Groq's hosted inference API.

The application sends the original agreement and the client's new request to the model. The model produces a structured analysis containing the scope status, reasoning, agreement evidence, and recommended action.

The application then presents that result through a task-specific interface rather than exposing the user to a raw model response.

The model runs through hosted inference in the current version; it is not running locally on the user's machine.

2. Evidence instead of a bare AI verdict

A freelancer needs to explain a scope decision, not merely receive a label.

That is why ScopeGuard AI includes relevant agreement excerpts and an interpretation of how they relate to the new request.

This makes the result more useful for reviewing the decision and preparing a client conversation. It also gives the freelancer an opportunity to check whether the model's reasoning makes sense.

The three possible outcomes are intentional. An unclear agreement should not automatically be treated as proof that a request is out of scope.

AI can assist with interpretation, but it cannot guarantee that an assessment is correct or replace professional legal advice. The freelancer remains responsible for reviewing the agreement, evidence, and suggested response.

3. From analysis to action

The application supports a practical workflow beyond the initial classification:

• Scope analysis: Compare the client request against the original agreement.
• Evidence review: Inspect relevant agreement text and the model's interpretation.
• Client communication: Generate a professional reply based on the analysis.
• Change-order preparation: Draft a proposal for additional work.
• Fee calculation: Calculate a proposed fee from estimated hours and the hourly rate.
• History: Revisit saved analyses through browser storage and authenticated cloud history.

These features connect the AI analysis to the decisions freelancers need to make in real client projects.

4. The technology stack

The project combines the following technologies:

• Next.js and TypeScript — application framework, UI, and server-side API routes.
• Tailwind CSS — responsive interface styling.
• Groq and gpt-oss-20b — hosted inference for AI-powered scope analysis.
• Clerk — user authentication.
• MongoDB Atlas — persistent history storage.
• Browser local storage — local history.
• Vercel and Render — application deployments.

I also built a demo scenario to make the project easier to evaluate without requiring every visitor to prepare their own agreement and client request.

5. Building for a real workflow

One of the key design decisions was to keep the analysis connected to what the user can do next.

A scope classification on its own is not enough. Freelancers also need to explain the decision, respond to the client, and estimate the cost of additional work.

ScopeGuard AI brings those steps together in one interface.

The current implementation is a working prototype. Its next stage should involve testing with real freelancers, collecting feedback, and evaluating the model against a representative set of agreements and change requests.

Why Does Open Innovation Matter?

For ScopeGuard AI, open innovation is about flexibility, experimentation, and the ability to build around a model that is not limited to a single closed-model ecosystem.

Using an open-weight model gives the project a model choice that can be evaluated, replaced, or potentially self-hosted in a future version, subject to the model's license and deployment requirements.

That flexibility matters for a product that may handle sensitive business information, such as client agreements, project requirements, and pricing discussions.

There is an important distinction in the current implementation: ScopeGuard AI uses hosted inference through Groq. It does not currently run the model locally or guarantee that agreement text stays entirely on the user's device.

A future self-hosted deployment could provide a different level of infrastructure control, but that is a possibility for future development, not a feature I am claiming today.

Open-weight models also make it possible to experiment with alternatives and evaluate how well they handle the specific challenges of scope analysis:

• Can the model identify the agreement language relevant to a new request?
• Can it distinguish a clearly additional feature from an ambiguous requirement?
• Can it explain its reasoning in a way a freelancer can review?
• Can it recommend a useful next step without treating every change as an extra charge?

Those are the questions that matter for this product. The ability to evaluate and potentially change the underlying model gives the project room to improve as those questions are tested.

For a small product with a potential paid use case, that flexibility is valuable. It lets development focus on the user's actual problem instead of assuming that one model or provider will always be the right fit.

My Agent Session

I have not included an agent-session recording in this submission. If I create a session using DevRelay, I can add the resulting link here to show the development process.

My First User

I am currently looking to validate ScopeGuard AI with freelancers who regularly handle client projects.

I do not yet have a verified paying-customer story to share, so I would rather be transparent than present hypothetical feedback as a real testimonial.

The next milestone is to put the tool in front of potential users, understand how they currently handle scope changes, and learn whether evidence-backed analysis and change-order preparation solve a problem they would pay to address.

Prize Categories

I am entering the following partner categories:

• Best Use of Render: ScopeGuard AI is deployed as a hosted web application on Render.
• Best Use of MongoDB Atlas: MongoDB Atlas provides persistent storage for users' saved analysis history alongside the open-weight AI workflow.

Thank you for checking out ScopeGuard AI.

If you work as a freelancer or independent developer, I would be interested in your perspective: When a client asks for something that was never clearly agreed upon, how do you decide whether it belongs in the original scope?

That question is at the heart of ScopeGuard AI, and feedback from people who experience this problem is the most useful next step in making the project better.