✦ CASE STUDY
Atlas
A scalable AI backend powering real-time medical diagnosis assistance — 50M+ embeddings, sub-50ms query latency.
The Challenge
MedSync was building an AI-powered diagnostic assistant for clinics in Southeast Asia. Their prototype worked in the lab but fell apart under real-world load — 5-second query latency, frequent downtime, and no way to scale across 200+ clinics.
They needed an infrastructure partner who could design a backend that was fast, reliable, and HIPAA-compliant from day one.
Our Solution
We designed and deployed Atlas — a complete AI infrastructure stack:
- Vector database cluster (Pinecone) handling 50M+ medical embeddings with sub-50ms query time
- Model pipeline (AWS SageMaker) for real-time inference with auto-scaling
- Custom API gateway with rate limiting, caching, and failover
- Multi-region deployment for low-latency access across Southeast Asia
Tech Stack
Pinecone
FastAPI
AWS SageMaker
Docker
Kubernetes
Terraform
Redis
PostgreSQL
The Results
50msAvg query latency
50M+Embeddings indexed
99.9%Uptime SLA
200+Clinics served
What the client said
Atlas turned our prototype into a production system that actually works. The infrastructure ShiftLabs built handles our growth without breaking a sweat.
— Dr. Arjun Patel, CTO, MedSync Health