Building AI-Powered Products

Shipping AI features in production is less about the latest model and more about reliability, latency, and clear boundaries between AI and your app.

Start with the problem, not the model

We chose models and architectures based on user needs: summarization, search, or classification. Sometimes a small, fine-tuned model or a RAG stack beat a large general-purpose model on both cost and quality for our use case.

RAG when you need grounded answers

For knowledge-heavy products, retrieval-augmented generation (RAG) kept answers accurate and traceable. We indexed our content, retrieved relevant chunks, and passed them as context. That reduced hallucinations and made it easier to debug and improve over time.

APIs and fallbacks

We wrapped all model calls behind our own APIs. That gave us a single place to add retries, rate limits, and fallbacks when a provider was slow or down. Users rarely saw raw model failures.

If you're adding AI to an existing product, start with one flow, measure quality and latency, and expand from there.