●TypeScript + SaaS

AI integration in SaaS is fundamentally an infrastructure problem, not a model problem. The LLM providers (OpenAI, Anthropic) handle the hard ML work. Your challenge is building the infrastructure that makes AI features reliable, cost-effective, and properly isolated for multi-tenant environments. The architecture pattern that works is RAG (Retrieval-Augmented Generation). Instead of fine-tuning models on customer data—expensive, slow, and creates privacy concerns—you retrieve relevant context at query time and include it in the prompt. This means your AI features use your customers' actual data without that data entering model training. Tenant isolation is straightforward: separate vector namespaces per customer. Cost management is the make-or-break challenge for AI features in SaaS. A naive implementation that sends every user query to GPT-4 will cost hundreds of dollars daily for a few hundred users. Sustainable AI features require tiered routing (cheap models for simple tasks), aggressive caching (identical queries should hit cache), and usage controls (rate limits, per-tenant caps). These aren't optimizations—they're requirements. The multi-provider strategy isn't just about pricing negotiation. OpenAI has outages. Anthropic has different context window characteristics. Having both integrated means your AI features stay available when one provider has issues, and you can route specific tasks to whichever provider handles them better.

AI integration projects start with identifying the highest-value use cases. Not every feature benefits from AI, and the integration overhead means you should be selective. I help prioritize based on user value, feasibility, and cost characteristics. Once we identify the right features, the architecture follows a consistent pattern. A retrieval layer indexes relevant customer data into vector storage with proper tenant isolation. A prompt engineering layer structures inputs to get consistent, useful outputs. A response processing layer validates outputs, extracts structured data, and handles errors gracefully. Caching reduces costs and improves latency throughout. The evaluation infrastructure is as important as the feature code. I set up evaluation pipelines that measure quality on representative samples, run regression tests on prompt changes, and monitor production quality over time. This catches issues before users complain—LLM quality can drift as providers update models. For production deployment, I implement the reliability patterns that AI features require: circuit breakers when providers are slow, fallback to cheaper models or cached responses, graceful degradation that shows helpful messages instead of errors, and comprehensive logging for debugging quality issues. Cost monitoring and alerting ensures you're not surprised by bills. Testing AI features is challenging because outputs are non-deterministic. I use a combination of deterministic tests (model returns valid JSON, includes required fields), statistical tests (quality scores on evaluation sets), and human review for representative samples. The test suite gives confidence that changes don't regress quality.