Julian Block

Production AI systems require end-to-end architecture: vector ingestion pipelines, RLHF training loops, retrieval optimization, failure modes—all designed together. When you own the entire stack, you optimize at every layer: database-level latency, query-specific chunking strategies, system-wide failure handling.

These systems fail predictably: embedding drift, retrieval latency spikes, model hallucination under load, vector index corruption. The ones that survive are built with the entire data flow in mind, not just the model call.