An AI startup with 12,000 active users faced a critical issue: their RAG system took 28 seconds to respond, and users were churning. In 5 days, we deployed production infrastructure that reduced latency by 25x — and moved the models inside the client's own cluster, so legal documents stopped leaving for a third-party API.
The client is a B2B SaaS startup building an AI assistant for legal teams. The core product is a RAG system that searches 50,000+ legal documents and generates answers with citations.
At the time they reached us: the model ran on a single GPU server without load balancing, the vector database shared a host with the API, no caching, no monitoring. At peak load, latency reached 28 seconds.
Conducted a full infrastructure audit, measured bottlenecks (70% of time — embedding generation, 20% — vector search, 10% — LLM inference). Designed target architecture: service separation, dedicated embedding service, Qdrant instead of Chroma.
Deployed a 3-node Kubernetes cluster (2× CPU, 1× GPU A100 80GB). Configured Ingress, cert-manager, namespace isolation. Migrated vector database to Qdrant with re-indexing of 50,000 documents.
Replaced llama.cpp with vLLM using PagedAttention — throughput increased 4x. Deployed dedicated embedding service based on bge-large-en-v1.5 (local, no OpenAI API). Added semantic caching via Redis for repeated queries.
Set up GitHub Actions → ArgoCD pipeline with automatic rollback on metric degradation. Deployed Prometheus + Grafana with custom dashboards: latency percentiles, GPU utilization, cache hit rate, vector search QPS.
Ran load testing up to 500 RPS. Final metrics: p50 latency — 0.8s, p95 — 1.1s, p99 — 1.8s. Delivered full documentation, runbooks, Terraform code, and trained the client's team.
"We spent 3 months trying to optimize this ourselves. OpsCover solved the problem in 5 days. Now our users don't notice any delay — they just get answers."
The engagement started as a latency problem, but what got built is exactly what we now deliver as our flagship: models running inside the client's cluster, data staying in their perimeter, and the GPU bill under control.
vLLM with PagedAttention on the client's GPU nodes instead of llama.cpp on a single box — throughput up 4x.
Embeddings moved from the OpenAI API to a local bge-large-en-v1.5. Legal documents are processed only inside the client's own infrastructure.
Semantic caching via Redis plus proper serving cut inference cost by 62%, and the dashboards show GPU utilization in real time.
GitOps via ArgoCD with automatic rollback on metric degradation, Prometheus and Grafana, Terraform code, runbooks and training for the client's team.
This engagement was a fast, focused migration on an existing model. A full private LLM deployment from scratch is a separate offer with a 4–6 week timeline.
Tell us about your infrastructure — we'll assess the task and propose a plan within 24 hours.