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Cambio Labs AI Studio • Active Project

Grant Buddy: AI-Powered
Grant Assistant

AI agent that discovers relevant grants, analyzes organizational context, and generates draft applications for nonprofit organizations. Cambio Labs AI Studio project using Python, OpenAI API, and LangChain.

RAG
Retrieval-Augmented Generation
AI-Powered
OpenAI API Integration
LangChain
Advanced Orchestration
Nonprofit
Organization Impact
View on GitHub Technical Details

The Nonprofit Challenge

⚠️

Time-intensive research to identify relevant funding opportunities

📝

Complex application processes requiring specialized expertise

💸

Limited resources for dedicated grant writing staff

📊

Information overload from thousands of available grants

AI-Powered Solution

Grant Buddy is an intelligent AI agent that discovers, analyzes, and generates grant applications using organizational context and advanced machine learning.

10-15hr
Time Savings
∞
Scale Potential

Technical Architecture

Cutting-edge AI pipeline combining web scraping, semantic search, and LLM-powered generation

🔍

Grant Discovery

Web scraping & research

🧠

Context Analysis

RAG system processing

✨

AI Generation

LLM-powered drafting

📄

Output Delivery

Streamlined format

🔗

RAG Implementation

  • • Vector embeddings for organizational docs
  • • Semantic search for context retrieval
  • • Dynamic context injection
  • • Knowledge base integration
🤖

OpenAI API

  • • GPT-powered text generation
  • • Sophisticated prompt engineering
  • • Multi-stage refinement
  • • Quality scoring system
⚙️

LangChain

  • • Complex workflow management
  • • Chain-of-thought processing
  • • Error handling & retry logic
  • • Scalable architecture design

Results & Impact

Transforming nonprofit grant acquisition through intelligent automation

Technical Innovation

✓ End-to-end automation pipeline
✓ Production-ready deployment
✓ Advanced AI integration
✓ Scalable system architecture

Business Value

💰 10-15 hours saved per application
📈 Increased application volume
✨ Improved application quality
🎯 Strategic focus enablement

Key Technical Learnings

AI/ML Engineering

  • • RAG system design for domain-specific applications
  • • LLM prompt optimization for consistent outputs
  • • Vector database management and similarity search
  • • Production ML deployment best practices

Product Development

  • • User-centered design for nonprofit workflows
  • • Stakeholder feedback integration
  • • Scalability planning for multi-organization use
  • • Compliance considerations for transparency

Explore the Code

Dive deep into the technical implementation, architecture decisions, and AI engineering patterns

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