ClientRadar: Autonomous Freelance Contract Discovery & AI Proposal Engine

Ashraf Ahmed — Mon, 19 Oct 2026 06:06:33 +0000

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

What I Built

Freelance engineers and boutique consultants spend 10 to 15 hours every week manually scanning fragmented job boards, dodging low-rate listings, and writing custom proposals.

ClientRadar AI is an autonomous contract arbitrage and AI proposal engine that turns this unpaid grind into a 30-second workflow:

Live Multi-Platform Contract Discovery: Harnesses real-time contract listings across LinkedIn, ZipRecruiter, Indeed, and direct company boards with dynamic portal deep-links.
Dynamic Compensation & Recency Parsing: Intelligently extracts true hourly rates (e.g., $75 – $150 / hr, $170 – $200 / hr) or computes role-based market estimates instead of showing generic placeholders.
Multi-Factor Viability & Risk Audit: Evaluates contract viability (68–97/100) based on compensation transparency, 1099 vs. W2 terms, scope detail, and flags potential risks (such as unlisted budgets or roles posted over two weeks ago).
AI-Powered Pitch Synthesis: Leverages open-weight Google Gemma to draft structured, bespoke technical outreach proposals across three selectable strategies (Direct Technical, Fast 48h Turnaround, Strategic Retainer).
Personalized Freelancer Profiles: Injects consultant credentials (target rate, portfolio, specialties, name) directly into generated pitches with 1-click Markdown export.

Who Would Pay For It?

Independent software engineers, technical consultants, and boutique agency founders who currently pay $50–$150/month for lead scraping tools. ClientRadar delivers a private, open-source alternative that finds high-intent remote contracts and drafts winning proposals instantly.

Demo

Live Deployed App: https://clientradar.streamlit.app/
Repository: https://github.com/AshrafGalaxy/ClientRadar

How to Run Locally

git clone https://github.com/AshrafGalaxy/ClientRadar.git
cd ClientRadar
python -m venv .venv
.\.venv\Scripts\activate   # On Windows (or source .venv/bin/activate on Linux/Mac)
pip install -r requirements.txt
streamlit run app.py

Open http://localhost:8501 in your browser. Configure your API keys (SERPAPI_API_KEY and HUGGINGFACE_TOKEN) in a .env file or use the built-in demo engine.

Code

AshrafGalaxy / ClientRadar

Autonomous freelance lead-arbitrage engine

🎯 ClientRadar AI

Autonomous freelance lead-arbitrage engine powered by SerpApi and Google Gemma 2.
Built for the Hacktoberfest open source AI challenge.

💡 Overview

Freelancers and boutique technical consultants spend 10-15 hours every week scanning job boards, filtering low-budget listings, and drafting custom outreach pitches.

ClientRadar AI turns this manual grind into an autonomous, 30-second pipeline:

Live Discovery & Deep-Linking: Leverages SerpApi's google_jobs engine to harvest fresh, high-ticket tech contracts across LinkedIn, Indeed, ZipRecruiter, and company portals in real time with direct application deep-links.
Multi-Tier Viability & Risk Scoring: Google's open-weight Gemma 2 (google/gemma-2-2b-it) parses the job description, evaluates Scope Clarity (35 pts), Budget Alignment (35 pts), and Timeline Realism (30 pts), while auditing for scope-creep or equity-only red flags.
Custom Outreach Pitch Studio: Gemma 2 crafts tailored proposals with selectable pitch strategies (Direct Technical, Fast 48h Turnaround, or Strategic Retainer…

View on GitHub

Repository: https://github.com/AshrafGalaxy/ClientRadar

How I Built It

Core Framework & UI: Built with Python 3.11 and Streamlit, featuring a bespoke responsive interface with SVG icon systems, dark/light theme switching, and segmented preview controls.
Open-Source AI Model: Powered by Google's open-weight model Gemma (google/gemma-3-4b-it / google/gemma-2-2b-it) via the Hugging Face Serverless Router for structured viability audits and technical proposal generation.
Real-Time Job Harvesting: Integrated with the SerpApi Google Jobs Engine to harvest real-time job listings, parsing direct apply portals (e.g., generating direct Apply on LinkedIn ↗ and Apply on ZipRecruiter ↗ buttons).
Heuristic Viability Engine: Custom Python parsing pipeline extracting hourly compensation brackets, work-from-home eligibility, and risk signals before synthesizing client outreach.

Why Does Open Innovation Matter?

Open-source and open-weight models like Google Gemma democratize high-grade AI for independent developers:

Eliminating Vendor Lock-in & Unpredictable API Costs: Proprietary closed APIs enforce restrictive usage caps and unpredictable meter billing. Open-weights allow developers to run inference locally or via serverless endpoints without recurring commercial tolls.
Data Privacy for Client Communications: Client pitches, client names, hourly terms, and confidential project proposals never have to be stored or trained on by closed third-party providers.
True Customizability: Developers have full transparency into model prompts, system architectures, and fine-tuning pipelines.

My Agent Session

ClientRadar was built and optimized using Google Antigravity, leveraging agentic pair programming to:

Implement resilient IPv4 network adapters overcoming Windows IPv6 connection resets on external cloud APIs.
Design dynamic SerpApi data parsers for compensation brackets and multi-platform action buttons.
Build the dual-view proposal studio (Rendered Pitch vs. Editable Markdown) with 1-click export.

My First User

During initial testing with independent backend contractors:

The tool scanned live remote listings and surfaced a $170 – $200 / hr Python contract on Turing via LinkedIn/ZipRecruiter.
In less than 15 seconds, ClientRadar evaluated the scope viability at 97/100, verified clear tech alignment (Python, Async, Distributed Systems), and generated a tailored 3-paragraph outreach pitch ready to submit.
The tester praised the direct platform action buttons (Apply on LinkedIn ↗) and the ability to personalize commercial rates and GitHub portfolio links in the sidebar.

DEV Community: Ashraf Ahmed