Case Studies

A financial-services firm was drowning in ungoverned 'Shadow AI' — unmanaged tools and API keys leaking sensitive data and breaking compliance. They needed every employee on a secure, governed path to AI. Delivering that meant both a governed AI product and the reproducible, multi-environment cloud-native platform to run it on.

Skilled engineers were spending 60%+ of their time on repetitive L1 work — password resets, access requests, and alert triage — while support costs scaled linearly with every new client. SLA breaches surfaced only after they happened, and every new environment meant rebuilding onboarding, ticketing, and knowledge transfer from scratch.

A code-generation SaaS was running on self-managed EC2 instances that couldn't scale to meet demand, ran up high operational costs, and offered weak observability with no multi-AZ high availability. Sudden traffic spikes led to bottlenecks and service disruptions.

The application and every worker process ran together on a single host, and a sudden CPU spike forced a costly vertical bump from a t3.2xlarge to an m5.4xlarge just to stay online. The platform needed true horizontal auto-scaling and a handle on cost, without downtime for a service sending and receiving multiple messages per second.

A fast-growing mortgage technology company struggled with slow deployments, manual processes, and limited visibility into their serverless infrastructure across multiple environments.