Client Overview
GrowFlare, Inc. was an AI-driven sales and marketing intelligence platform that helped companies identify their best-fit prospects using predictive AI and psychographic data. The platform enabled businesses to discover accounts that looked like their best customers in seconds, streamlining lead generation, lead scoring, and account-based marketing efforts.
Project Background
Role: Technical Co-Founder
Duration: 2 years (idea to acquisition)
Outcome: 53x return on investment upon acquisition by Terminus
Company Vision
GrowFlare’s value proposition was clear: “Find prospects that look like your best customers in 5 seconds.” The platform used a unique blend of predictive AI and psychographic data to help businesses discover their best-fit accounts and communicate effectively with them, addressing common challenges in lead generation, account prioritization, and sales intelligence.
Technical Leadership and Development
As technical co-founder, I was responsible for building the entire technical infrastructure from concept to production-ready platform that served 300+ customers.
Large-Scale Web Scraping Infrastructure
I designed and implemented a comprehensive web scraping engine that:
- Scraped over 8 million websites weekly to gather prospect and company data
- Built scalable, resilient scraping architecture using AWS services
- Implemented data quality controls and validation systems
- Created efficient storage and processing pipelines for massive datasets
- Ensured compliance with web scraping best practices and legal requirements
Machine Learning and AI Development
Developed sophisticated 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 real-time AI-based predictive engine that delivered results in seconds
- Integrated multiple data sources to create comprehensive prospect profiles
- Continuously improved model accuracy through feedback loops and data refinement
Cloud Infrastructure and DevOps
Architected a robust, scalable infrastructure using:
- AWS Services: Leveraged multiple AWS services 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
Business Impact and Outcomes
- Revenue Growth: Bootstrapped and grew to 300+ paying customers
- Technical Scale: Successfully processed 8+ million websites weekly
- Performance: Delivered prospect identification results in under 5 seconds
- Market Validation: Achieved product-market fit in the competitive sales intelligence space
- Exit Success: 53x return on investment upon acquisition by Terminus in just two years
- Platform Reliability: Built infrastructure that scaled with rapid customer growth
- Data Quality: Maintained high-quality prospect data through automated validation systems
Key Achievements
- Designed architecture that could handle massive scale while maintaining sub-5-second response times
- Created machine learning models that accurately identified high-value prospects
- Built a technically sophisticated product that attracted acquisition interest from industry leaders
- Demonstrated the ability to scale from zero to 300+ customers through robust technical execution
- Proved the viability of AI-driven sales intelligence in the B2B market
This engagement showcased our ability to take a concept from ideation through to successful acquisition, combining technical expertise in machine learning, large-scale data processing, and modern cloud infrastructure to create significant business value.