Data & ML infrastructure that stays fast as it grows
High-throughput pipelines and serving infrastructure engineered so latency stays flat while workloads multiply.
Sector: HR-tech / analytics · details anonymized
The challenge
Data volumes and model workloads were growing faster than the systems serving them. Every quarter added latency, cost and operational fires.
The mandate: re-architect for scale without pausing the product roadmap.
Our approach
Incremental strangler-style migration. New pipelines shipped alongside the old ones with shadow traffic validating parity before cutover.
Observability first, because you can't fix a system you can't see. Metrics, tracing and cost attribution landed before the refactor did.
Architecture
Streaming ingestion with idempotent, replayable processing stages instead of fragile batch jobs.
Horizontally scalable services behind queues, so spikes buffer instead of cascading.
Vector and analytical stores tuned per workload, with caching layers where the read patterns earned them.
Outcome
The platform absorbed workload growth without the latency curve bending upward.
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