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AI SaaS products introduce type challenges not covered in standard TypeScript guides. Modeling uncertain AI outputs. Typed prompt templates. AI pipeline state modeling. Runtime validation for model responses. These are the patterns refined across 8 production products.
I have shipped 8 vertical AI products. The fastest took 18 days. This is the technical process. What to build each week. What decisions to make early. What to delay. Code patterns that allow fast execution without technical debt.
Building a pipeline that ingests sensor data from more than 1.2 million acres, combines satellite imagery, weather feeds, and commodity markets, and produces agronomic recommendations in near real time. This is the architecture.
PIPEDA, Quebec Law 25, AIDA, data residency requirements. Building AI for Canadian healthcare, insurance, finance, or government requires navigating strict regulations. This is a practical technical implementation guide.
Running 8 AI products in production means AI API costs are a significant operating expense. Over 18 months I developed a systematic cost optimization approach that reduced costs by 60 percent while maintaining or improving output quality. Here is every technique I use.
Server Components, Route Handlers, streaming, and edge patterns that make AI SaaS fast and reliable in production. Focused on latency, streaming responses, and background processing.
Most prompt engineering content focuses on getting better outputs. Production prompt engineering focuses on reliability, consistency, cost control, and failure handling at scale across real SaaS products.
Running 8 multi-tenant AI products means 8 systems where one bug could expose one customer’s data to another. Here is the complete Row Level Security architecture I use to make that impossible at the database level.
GPT-4o vs Llama, Claude vs Mistral. Model selection debates are full of opinions and few real frameworks. This is the decision process I use across 8 production AI products.
