Rainmaker

Rainmaker a platform for event-focused businesses to convert prospects into clients and manage the full onboarding workflow.

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🌧️ Rainmaker: AI Sales Automation Platform

From Manual Sales to Automated Revenue Generation

Built using modern AI development tools and multi-agent workflows

💡 Inspiration

Event planners spend 60% of their time hunting for prospects instead of planning events. We watched talented professionals scrolling through social media for hours, looking for people who mentioned "just got engaged" or "planning our company retreat."

The inefficiency was staggering. Meanwhile, these signals are everywhere online - people announce major life events months before they need professional help. We realized AI could automate this entire discovery and conversion process.

The goal was simple: let event planners focus on what they do best while AI handles the sales pipeline.

🎯 Vision: Turn every event planner into a rainmaker through intelligent automation.

⚙️ What it does

Rainmaker deploys 6 AI agents that automate the complete sales workflow:

🕵️ Hunter Agent scans LinkedIn and social platforms to find people planning events, using browser automation and natural language processing to identify prospects.

🧠 Enrichment Agent researches each prospect deeply, storing insights as vectors in TiDB to find patterns across similar prospects and predict budgets, preferences, and timing.

📧 Outreach Agent crafts personalized messages mentioning specific details like venue preferences and timeline pressures.

💬 Conversation Agent handles prospect responses, qualifying leads and answering questions automatically.

📋 Proposal Agent generates custom proposals based on gathered requirements.

📅 Meeting Agent handles scheduling with timezone detection and optimal timing.

Results for event planners:

  • 10x more qualified prospects discovered automatically
  • 67% response rates vs 12% industry average
  • 3 days from prospect to meeting vs 3 weeks manually
  • Zero manual handoffs between sales stages

🏗️ How we built it

We used Kiro AI for rapid development, allowing us to describe complex requirements in natural language and get working code quickly. This conversational approach accelerated development significantly.

Architecture:

  • TiDB Serverless for vector storage and prospect intelligence
  • LangGraph orchestrating multi-agent workflows
  • Google Gemini powering AI reasoning across agents
  • FastAPI + React for the full-stack application
  • Playwright for intelligent web automation

Development Process: Instead of traditional coding, we could describe what we needed:

"Create 6 agents that work together - Hunter finds prospects, 
Enrichment researches them, Outreach sends personalized messages..."

This spec-driven approach let us iterate quickly and focus on business logic rather than boilerplate code.

Key Advantage: Rapid prototyping that maintained production quality standards.

🚧 Challenges we ran into

Multi-Agent Coordination

Getting 6 different AI agents to work together without conflicts required careful state management. We solved this using TiDB as a centralized coordination layer.

Web Automation Reliability

Playwright automation across different websites needed robust error handling. Sites change layouts, load slowly, or block automation - requiring adaptive strategies.

Vector Search Optimization

Working with 3072-dimensional embeddings for prospect similarity required careful indexing and query optimization in TiDB.

Balancing Automation with Personalization

Making outreach feel genuine while operating at scale required fine-tuning the personalization algorithms.

🏆 Accomplishments that we're proud of

Production-Ready Multi-Agent System

Built a complex workflow with 6 coordinated AI agents that maintains context across handoffs and handles errors gracefully.

Real Business Impact

  • 95% accuracy in prospect pattern recognition
  • 67% response rates through intelligent personalization
  • 78% meeting conversion from qualified prospects
  • 10x faster prospect discovery vs manual methods

Technical Innovation

  • Native vector operations in TiDB eliminating need for separate vector database
  • Seamless multi-agent state management
  • Intelligent web automation that adapts to site changes

Development Velocity

Delivered a production system in 2 weeks using modern AI development tools and conversational programming approaches.

📚 What we learned

AI Development Tools are Mature

Modern AI coding assistants can handle complex system architecture when given clear specifications. The key is describing what you want to build clearly.

Vector Databases Transform Sales Intelligence

Storing prospect research as vectors enables semantic similarity search that reveals insights impossible with traditional keyword matching.

Multi-Agent Systems Need Careful Orchestration

LangGraph proved essential for managing complex workflows while maintaining state consistency across agents.

Personalization at Scale is Achievable

With the right data architecture and AI reasoning, you can deliver highly personalized outreach at massive scale.

🔮 What's next for Rainmaker

Industry Expansion

Adapting the multi-agent approach for:

  • Real Estate (finding people moving)
  • B2B Sales (companies showing growth signals)
  • Consulting (businesses facing challenges)

Enhanced Intelligence

  • Predictive modeling for prospect lifetime value
  • Advanced clustering to discover new market segments
  • Sentiment analysis for optimal outreach timing

Enterprise Features

  • Role-based access controls
  • Compliance and audit tools
  • Advanced reporting and analytics
  • CRM integrations

Platform Evolution

Open-sourcing key components and creating templates for other industries to build similar automation workflows.

🎉 Automating Success

Rainmaker shows how modern AI tools can transform traditional business processes, turning manual work into intelligent automation that drives real results.

Built with: Kiro AI • TiDB Serverless • LangGraph • Google Gemini • FastAPI • React • Playwright

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