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.
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. InfoScale solved the problem in 5 days. Now our users don't notice any delay — they just get answers."
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