Automated Lead Acquisition Pipeline

The Problem

Professional service providers like Real Estate Agents often waste 10+ hours per week manually searching for leads, parsing contact info from unstructured web pages, and organizing them into spreadsheets.

The Solution

I engineered a fully automated, local-first infrastructure that scrapes target data, uses LLMs to qualify leads based on intent, and saves the cleaned output directly into structured formats. This removes 95% of manual administrative workload.

Architecture and Tech Satck

Core: Python, Bash, Git.

Pipeline: Playwright/Firecrawl (Data Collection), LLMs (Qualification/Processing), Pandas (Data Cleaning).

Security: All sensitive credentials are handled via local .env configuration, ensuring data privacy and enterprise-grade security.

Deployment: Containerized via Docker to ensure cross-platform environment consistency.

Key Capabilities

Modular Design: Independent modules for Collection, Processing, and Storage, allowing for easy updates as business requirements evolve.

Robust Error Handling: Designed with failure-resilience, including automated logging and retry logic for high-reliability operations.

Scalable Data Processing: Capable of handling high-volume data bursts without system degradation

Email Automation

Manual email management is a massive drain for professional service providers. It forces highly skilled workers to spend hours on repetitive, low-value tasks.

The solution

I engineered a fully automated, local-first infrastructure

Getting Started

# 1. Clone the repository
git clone [https://github.com/yourusername/your-repo-name.git](https://github.com/yourusername/your-repo-name.git)

# 2. Configure environment variables
cp .env.example .env 
# Add your API keys and configuration settings to the .env file

# 3. Install dependencies
pip install -r requirements.txt

# 4. Execute the pipeline
python main.py