Device - Macbook Air

Challenge / Problem Statement

The freelancing marketplace faced multiple challenges:

  • Manual proposal writing led to inconsistent communication and low engagement.
  • Freelancers struggled to identify relevant leads among hundreds of postings.
  • Job requirements were often unclear, reducing matching accuracy.
  • Generic outreach messages caused low conversion rates.
  • Scaling personalized outreach remained difficult.

Objectives

  • Automate real-time matching between job postings and freelancer profiles.
  • Generate personalized proposals tailored to each opportunity.
  • Improve client response rates and project acquisition.
  • Reduce freelancers’ manual effort in writing proposals.
  • Enable scalable outreach without losing personalization.

Process & Implementation

  • Built job scrapers to collect postings in real time via APIs/webhooks.
  • Structured & cleaned job and freelancer profile data (Python, Pandas).
  • Integrated a RAG pipeline combining job descriptions with skill sets.
  • Applied prompt engineering for tone, domain, and client-specific personalization.
  • Used LLMs (OpenAI GPT) for human-like message generation.
  • Added automation for sending responses and tracking results.

Our Solution

We engineered an AI-powered RAG pipeline that automated the entire lead-generation workflow:

  • Scraped and monitored live job postings from freelancing platforms.
  • Retrieved relevant freelancer profiles matched to each job description.
  • Applied dynamic prompt engineering with LLMs to generate tailored outreach messages.
  • Automated delivery of proposals with built-in tracking for engagement metrics.
  • Designed the solution to be scalable across freelance domains and client types.

Tools & Tech Used

  • Backend: Python, Flask/FastAPI
  • AI/LLMs: OpenAI GPT, RAG
  • Data Processing: Pandas
  • Scraping: APIs / Web scraping libraries
  • Messaging: JSON & Markdown templates
  • Automation: WebSockets, Celery

Results & Impact

  • 3x improvement in client response rates
  • 80% reduction in freelancer proposal-writing effort
  • Highly personalized, context-driven messages for each lead
  • Real-time lead matching improved first-contact timing and conversion
  • End-to-end automated pipeline for scalable lead generation

Key Takeaways

  • RAG pipelines combined with LLMs can revolutionize sales automation.
  • Real-time lead generation ensures faster client engagement.
  • MoreYeahs delivers scalable AI-driven solutions that improve conversions and reduce effort.

Project Duration & Team

  • Duration: 6 weeks
  • Team:1 AI/ML Engineer, 1 Backend Developer, 1 Tester
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