I Built a Complete AI Infrastructure Stack from Scratch — Here's What I Learned Most AI projects start at the top of the stack. You grab an LLM API, wire up a vector database, build a RAG pipeline, and ship. That works — until it doesn't. Until your training job crashes at hour 6. Until your inference cache fills up and nobody knows why. Until a worker dies mid-processing and your embeddings are corrupted. I wanted to understand what happens below the API layer. So I built the whole thing from scratch. The Stack Over the past few months I built four interconnected systems that form a complete AI infrastructure stack: VeriStore → Storage layer (WAL, Raft, crash recovery) ↓ llm-serving-cache → Inference serving (KV cache, GPU memory, routing) ↓ Veriflow → Workload orchestration (training jobs, checkpoints, GPU scheduling) ↓ SmartSearch → AI data pipeline (async ingestion, Kafka, RAG, fault tolerance) Enter fullscreen mode Exit fullscreen mode Each layer depends on the one below it.…