WorkMark

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

WorkMark — Know the work. Mark the change.

🧩 What I Built

A project rarely expands all at once. Sometimes, it begins with a seemingly harmless request: one extra feature, another revision, or a small addition that gradually becomes part of the expected work.

For freelancers, these changes can be difficult to track against the original agreement. What was initially promised gets buried beneath conversations, evolving requirements, and new expectations.

WorkMark is an AI-powered scope-change assistant designed to help freelancers recognise these changes before they become costly.

It compares incoming client requests with the original project scope, identifies potentially new work, distinguishes additions from clarifications, and explains the reasoning behind each assessment. It also helps freelancers draft professional responses to discuss changes with their clients.

Rather than treating every new request as additional work, WorkMark brings the original agreement back into the conversation.

The idea is simple: know what was agreed, understand what changed, and respond with clarity.

WorkMark is designed for independent developers, designers, consultants, and small agencies who want to manage evolving project requirements without compromising their client relationships.

📋 Demo

The core experience centres on comparing an original agreement with a new client request, highlighting potential scope changes, and presenting the evidence behind each finding.

The best way to experience WorkMark is to give it an original project brief and a new client request. See what was already agreed, identify what might have changed, and prepare a response with confidence.

💻 Code

🔗 GitHub Repository: https://github.com/rrane1307/WorkMark

WorkMark is being developed with an emphasis on making scope changes understandable, keeping AI-generated assessments transparent, and giving freelancers control over decisions that affect their work.

🛠️ How I Built It

I approached WorkMark as a focused decision-support tool rather than another all-in-one project-management platform.

The experience centres on a simple workflow: establish the original scope, analyse incoming requests, inspect the differences, and decide how to respond.

AI helps interpret language and compare requirements, while structured data, validation, and persistent records make the results reviewable and actionable.

Technology stack:

• Frontend: React 18 with TypeScript
• Styling: Custom responsive CSS with Tailwind CSS configuration
• Build tool: Vite
• Routing and server state: React Router 6 and TanStack Query 5
• Input validation: Zod on the frontend and Pydantic 2 in the backend API
• Backend: Python with FastAPI and Uvicorn
• Database: SQLite with SQLAlchemy 2
• AI analysis: Optional open-weight language model through an OpenAI-compatible chat-completions API, with deterministic demo analysis when no provider is configured or the provider fails
• Icons: Lucide React
• Testing: Vitest, React Testing Library, jsdom, and pytest

The architecture separates the AI integration from the application's business logic. This allows the analysis pipeline to validate structured model outputs, preserve supporting evidence, handle uncertainty, and store user decisions.

One design decision guides the project throughout: AI should help freelancers understand changing requirements, not make contractual decisions on their behalf.

WorkMark therefore distinguishes potentially new work from included requirements, clarifications, and ambiguous requests. Users can review the reasoning, change classifications, and edit generated responses before using them.

🌍 Why Does Open Innovation Matter?

Tools that influence professional decisions should make their reasoning easier to inspect, not harder to question.

Open innovation creates opportunities to experiment with open-weight models, adapt AI workflows, and build useful tools without depending entirely on a single proprietary ecosystem.

For WorkMark, this matters because analysing project scope requires more than generating convincing text. The system must compare requirements, preserve evidence, acknowledge uncertainty, and leave important decisions in human hands.

An open and adaptable architecture makes it possible for other developers to experiment with different models, improve requirement extraction, refine classification strategies, and explore better ways to connect client requests with original agreements.

The goal isn't to add AI to every step. It's to use AI where language and context make a difference, while keeping validation, record-keeping, and user decisions transparent and dependable.

🏆 Prize Categories

• Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle (Primary Track)
• Relevant open-weight AI or partner technology categories, if applicable