§ Case study
GrowFlare, Inc.
Client Overview
GrowFlare, Inc. was an AI-driven sales and marketing intelligence platform. Companies used it to identify their best-fit prospects using predictive AI and psychographic data, discovering accounts that looked like their best customers in seconds.
Project Background
Role: Technical Co-Founder
Duration: 2 years (idea to acquisition)
Outcome: 53x return on investment upon acquisition by Terminus
The Problem
Sales teams waste time on prospects who will never convert. GrowFlare’s value proposition: “Find prospects that look like your best customers in 5 seconds.” The platform used predictive AI and psychographic data to help businesses discover their best-fit accounts.
What We Built
As technical co-founder, I built the entire technical infrastructure from concept to production. The platform served 300+ customers.
Web Scraping Infrastructure
I designed and implemented a web scraping engine that:
- Scraped over 8 million websites weekly to gather prospect and company data
- Built scraping architecture using AWS services that handled failures gracefully
- Implemented data quality controls and validation systems
- Created storage and processing pipelines for massive datasets
- Ensured compliance with web scraping best practices and legal requirements
Machine Learning and AI
I developed machine learning algorithms that:
- Created predictive models to identify best-fit buyers based on existing customer patterns
- Implemented psychographic analysis to understand prospect behavior and preferences
- Built a real-time predictive engine that delivered results in seconds
- Integrated multiple data sources to create comprehensive prospect profiles
- Improved model accuracy through feedback loops and data refinement
Cloud Infrastructure
I architected infrastructure using:
- AWS Services: EC2, S3, RDS, and Lambda for compute, storage, and data processing
- Containerization: Docker for consistent deployment and scaling
- Infrastructure as Code: Terraform for reproducible, version-controlled infrastructure
- Container Orchestration: Amazon ECS for managing containerized applications
- CI/CD Pipeline: GitHub Actions for automated testing, building, and deployment
- Database Systems: PostgreSQL for structured data storage
- Search and Analytics: Elasticsearch for fast data retrieval and analytics
Technologies Used
- Web Scraping: Python-based scraping framework
- Machine Learning: Custom algorithms for predictive modeling and psychographic analysis
- Cloud Platform: Amazon Web Services (AWS)
- Containerization: Docker and Amazon ECS
- Infrastructure: Terraform for infrastructure as code
- Databases: PostgreSQL for data persistence
- Search Engine: Elasticsearch for real-time data queries
- CI/CD: GitHub Actions for automated deployment
- Data Processing: Scalable ETL pipelines for processing millions of data points
Outcomes
- Grew to 300+ paying customers
- Processed 8+ million websites weekly
- Delivered prospect identification results in under 5 seconds
- Achieved product-market fit in the competitive sales intelligence space
- 53x return on investment upon acquisition by Terminus in two years
- Built infrastructure that scaled with rapid customer growth
- Maintained high-quality prospect data through automated validation systems
Key Achievements
- Designed architecture that handled massive scale while maintaining sub-5-second response times
- Created machine learning models that accurately identified high-value prospects
- Built a product that attracted acquisition interest from industry leaders
- Scaled from zero to 300+ customers
- Proved the viability of AI-driven sales intelligence in the B2B market
This engagement shows what happens when you combine machine learning, large-scale data processing, and modern cloud infrastructure, and ship.