Case Studies
A look at how we help teams scale, modernize, and operate LLM inference in production — with the architecture decisions and measurable results behind each engagement.
Re-architected a single-replica inference server into a horizontally scalable vLLM + Ray Serve platform built to handle production launch traffic with predictable tail latency.
Key Outcomes
Led an end-to-end migration from a legacy Kubeflow environment to an enterprise inference platform on Ray, cloud Kubernetes, vLLM, and NVIDIA Triton — a unified GPU + TPU serving fabric delivered with zero production downtime.
Added LLM-specific observability, structured logging, distributed tracing, Kubernetes-native autoscaling, SLO-based alerting, and incident response — turning a working platform into a production-operable service.
Redesigned networking, scheduling, and model placement around the physical GPU interconnect topology — keeping tensor-parallel traffic on NVLink/NVSwitch and pipeline stages on InfiniBand/RDMA — so the interconnect stopped bottlenecking large-model serving.
Work With Us
Tell us where your infrastructure is today and we'll map out what's possible — from a single assessment to a full-scale production engagement.