built SubjectSensei (
https://subjectsensei.com) to solve my own problems with cold emailing and business development. As someone with minimal coding experience, I wanted to share how AI coding assistants enabled me to build a fully-featured SaaS product and what I learned along the way.
What it does:
Analyzes email content for clarity, persuasion, readability, CTA effectiveness, and tone
Generates and A/B tests email subject lines using LLM optimization
Researches target companies (via Perplexity API) for personalized outreach
Analyzes technology stacks for better prospect targeting
Personalizes email templates based on company research
Technical stack:
Flask backend with RESTful API endpoints
Supabase for authentication and database
Integration with multiple AI providers (OpenAI, Perplexity, Llama models)
Lemon Squeezy for subscription management
Usage tracking and multi-tier access controls
Engineering challenges I solved:
Building a prompt engineering system that consistently delivers structured JSON from different LLM APIs
Implementing citation tracking for company research to maintain information accuracy
Creating a usage tracking system that enforces limits based on subscription tiers
Building a secure authentication flow with Supabase
What I learned:
How to effectively use AI coding assistants to build beyond my skill level
The importance of API abstraction layers when working with multiple LLM providers
How to implement proper error handling for LLM responses
Building secure authentication systems
Monetizing a product from day one
I'm happy to answer any questions about the technical implementation, how I used AI to help me build it, or my experience creating a SaaS product as a non-developer.
What it does:
Analyzes email content for clarity, persuasion, readability, CTA effectiveness, and tone Generates and A/B tests email subject lines using LLM optimization Researches target companies (via Perplexity API) for personalized outreach Analyzes technology stacks for better prospect targeting Personalizes email templates based on company research Technical stack:
Flask backend with RESTful API endpoints Supabase for authentication and database Integration with multiple AI providers (OpenAI, Perplexity, Llama models) Lemon Squeezy for subscription management Usage tracking and multi-tier access controls Engineering challenges I solved:
Building a prompt engineering system that consistently delivers structured JSON from different LLM APIs Implementing citation tracking for company research to maintain information accuracy Creating a usage tracking system that enforces limits based on subscription tiers Building a secure authentication flow with Supabase What I learned:
How to effectively use AI coding assistants to build beyond my skill level The importance of API abstraction layers when working with multiple LLM providers How to implement proper error handling for LLM responses Building secure authentication systems Monetizing a product from day one I'm happy to answer any questions about the technical implementation, how I used AI to help me build it, or my experience creating a SaaS product as a non-developer.