When your image analysis, search embeddings, and chat assistant all share one queue, you have a deli line problem. The customer who wants one slice waits behind the catering order.

When I was CTO of an AI startup, I built a multi-provider AI pipeline on Convex. It used priority queuing, circuit breakers, and adaptive batching. The same real-time architecture served a web app (TanStack Start) and a mobile app (React Native) with shared business logic.

I'll walk through the design, the failures that shaped it, and how to tell which of your AI jobs belong at the front of the line.

Adaptable to 20, 30, or 45 minutes. Target audience: full-stack and backend engineers adding several AI features to one product. Companion talk to "From Pixels to Meaning," delivered at the AI Community Conference - New York, June 2026.