Problems we've been trusted with.

The problem

A production ML model powering fraud detection was serving predictions through a brittle, monolithic inference service. As request volume grew, p99 latency ballooned past 4 seconds. Deploys took a full afternoon and required manual rollbacks on roughly 30% of releases.

What we built

We rebuilt the serving layer as a purpose-built inference service with model versioning, canary rollouts, and structured observability. Introduced async pre-processing to eliminate the main bottleneck. Rewrote the deploy pipeline with automated rollback triggers tied to latency SLOs.

Outcome

p99 latency dropped from 4.2s to under 900ms. Deploys went from afternoon events to 12-minute automated runs. The ML team now owns and maintains the service independently.

The problem

A fast-growing e-commerce platform was deploying multiple times per week into a fragile, manually managed infrastructure. Each release took 45+ minutes, required two senior engineers on standby, and had a roughly 25% rollback rate. The team had stopped shipping features confidently.

What we built

Migrated workloads to a Kubernetes-based deployment architecture with GitOps workflows, automated blue-green deploys, and comprehensive rollback triggers. Introduced environment parity between staging and production to eliminate "works on staging" issues. Built a shared internal platform library that standardised service configuration across 11 microservices.

Outcome

Mean time to deploy dropped from 45 minutes to under 3. Rollback rate dropped to near zero. Within 60 days the engineering team was shipping 2x as many features with the same headcount.