Case study · Industrial / Geospatial
Large-Scale Image Processing Pipeline
High-throughput vision pipeline processing tens of terabytes of imagery daily across distributed inference nodes — with the observability and routing layer to keep p95 latency tight.
Computer VisionInfrastructureManaged Services
40TB+
Imagery processed daily
99.97%
Pipeline uptime
<200ms
p95 inference latency
Challenge
The customer's existing pipeline buckled under burst loads and lacked the observability to diagnose stalls. Storage, inference, and post-processing ran on separate clocks with no shared SLOs.
Approach
- Streaming ingest with backpressure, partitioned object storage, and content-addressed deduplication.
- Distributed inference cluster with autoscaling, GPU pooling, and fallback CPU routing for tail latency.
- End-to-end tracing, SLO dashboards, and on-call rotation tied to a documented runbook.
- Cost telemetry per job class so engineering can tune workloads against budget envelopes.
Outcome
Pipeline now ingests and processes 40TB+ daily with 99.97% uptime and predictable p95 latency. The ops team handles incidents in minutes instead of hours.
Got an idea? Let's make it real.
Tell us about your problem. We'll come back within one business day with a take, a rough plan, and a call invite.