What I Built
Freelancers spend time turning client requirements into proposals, defining project scope, planning milestones, estimating costs, and preparing invoices. FreelanceFlow AI is a local-first AI assistant designed to help organise that work in one place.
The application takes a client brief and helps generate structured, editable drafts for:
- Client-brief analysis, including requirements, assumptions, gaps, and risks
- Project proposals
- Scope of work
- Project milestones
- Invoice drafts
- Pricing estimates based on user-provided hours, rates, and costs
Freelancers can review and edit the generated content before using it and export their work as Markdown.
The intended users are independent freelancers and small service businesses. A potential paid version could help them streamline project preparation, but I have not yet validated willingness to pay with real users.
Demo
FreelanceFlow AI demo video: Upload https://drive.google.com/file/d/17gT57SUZmxA-VX6w74tjAvISDMdwtGmn/view?usp=sharing directly to this DEV post using the editor's media-upload option, or provide a publicly accessible video URL.
The video demonstrates the local application using a sample bakery client brief. It is a local prototype demonstration, not a publicly deployed application.
Code
GitHub repository: https://github.com/shrey04kaitke-sys/FreelanceFlowAI
The repository contains the project code and documentation.
How I Built It
FreelanceFlow AI combines a React, TypeScript, Vite, and Tailwind frontend with a FastAPI backend.
For open-source AI, I use Gemma 3 1B through Ollama for local inference. The model assists with analysing client briefs and generating draft project documents. The pricing estimator uses deterministic calculations rather than relying on the language model for arithmetic.
The application also includes editable outputs, Markdown export, model-status information, and optional local history.
The current implementation is a prototype. Some generated content still appears in JSON-style editable fields, and the document presentation needs further refinement. Generated drafts require human review before they are shared with clients.
Why Does Open Innovation Matter?
Open innovation makes it possible to experiment with AI workflows using open-weight models and locally controlled inference rather than depending entirely on a proprietary hosted-model API.
Using Gemma through Ollama lets this prototype run its inference locally, helping reduce dependence on external inference services and giving developers greater control over the model runtime. It also makes experimentation with prompts, output structures, and alternative models more accessible.
This approach does not automatically guarantee privacy or security in every configuration. Local services must be protected from unauthorised access, and data handling must be considered before any public deployment.
Open-source tools also make the project easier for other developers to inspect, learn from, adapt, and improve. I want FreelanceFlow AI to be something that can evolve through practical feedback and community contributions.
Prize Categories
- Best Use of Gemma — The project uses Gemma 3 1B through Ollama for its AI-assisted brief analysis and document-generation workflow.
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