Intelligence Forge

The client's ML team was bottlenecked by a homegrown inference pipeline that couldn't handle a projected 10× traffic spike. Manual batching meant 800ms average latency — killing the product's real-time promise before it launched.

We rebuilt the inference stack around a fine-tuned LLM with a custom neural API layer. Edge deployment via distributed workers resolved requests at the nearest node. Ray Serve handled dynamic batching and auto-scaling without configuration overhead. The entire system was observable from day one — every inference logged, every bottleneck visible.

2M+ daily inferences processed with sub-100ms average latency. Infrastructure costs dropped 4× through efficient batching. The team shipped their AI feature to production in week 8 — three weeks ahead of the original estimate.