Case Studies
Real engagements. Measurable outcomes. Every metric below is from a production deployment.
Series B SaaS Company
The Problem
Their fraud detection model worked in staging. Six months later, still not in production. The ML team was blocked by infrastructure, and the platform team was drowning in on-call rotations.
What We Did
We deployed a multi-agent AI pipeline with a working MVP in 1 week: containerised model serving, automated retraining with drift detection, A/B testing, and Kubernetes auto-scaling, all governed by agentic SRE monitoring.
Results
FinTech, $40M ARR
The Problem
AWS bill hit $65K/month. The CFO wanted a breakdown by team and project. The CTO had no answer, just 'that's what the models cost.' Zero visibility, zero accountability.
What We Did
Our AI-powered FinOps audit identified $39K in waste: oversized GPU instances, idle dev environments, and mismatched reservations. Deployed agentic cost monitoring that catches anomalies in real time.
Results
Enterprise Data Platform
The Problem
Every deployment required 2 weeks, 3 teams, and a change advisory board. Friday deploys meant weekend work. Engineer attrition hit 30%. Something had to change.
What We Did
We built an agentic SRE pipeline: GitOps with canary deployments, instant automated rollbacks, and Slack-based AI monitoring. Engineers push code. Our agents handle everything else.