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.

An agricultural e-commerce and analytics platform ran on a legacy managed-Kubernetes setup with weak governance, inconsistent tagging, and thin security controls — putting it at risk of CIS/GDPR non-compliance while manual operations and overprovisioning drove up cost and slowed incident response.

Marketing video production was slow, costly, and dependent on specialized creative talent, taking 2-3 days per video and making it impossible to scale content across campaigns, regions, and languages. The client needed to turn a simple text prompt into a publish-ready, brand-aligned video in minutes.

An insurance claims platform was drowning in slow, manual claim processing — long turnaround times, high operational cost, inconsistent decisions, and limited fraud detection. They needed to transition to an automated, scalable GenAI workflow without sacrificing compliance or explainability.

The platform's generic, one-size-fits-all programs left members disengaged, with roughly 65% of new subscribers dropping off within three months. There was no personalized guidance, no consistent coaching, and no real-time feedback — and no cloud foundation capable of supporting AI personalization at scale.

As the platform moved to adopt Generative AI across its hiring and talent workflows, it lacked a structured approach to model selection, prompt design, and governance — leaving AI outputs inconsistent and raising concerns around bias, fairness, and explainability.

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.

Customer service scaled linearly with headcount, and a lean investor-relations and operations team was drowning in repetitive, high-touch inquiries — account lookups, balance checks, KYC and document requests. Manual handling created compliance exposure, and clients across time zones had no after-hours coverage.

A healthcare SaaS provider held vast amounts of sensitive data — medical records, insurance claims, and lab results — fragmented across systems with inconsistent security and manual processes. They needed to secure this data and meet HIPAA compliance without disrupting operations.

The platform ran on a single Ubuntu VM with a self-managed MySQL database and SFTP-based releases, capping it at roughly 2,000 concurrent users and causing frequent overloads and downtime during traffic spikes. Deployments took 2-3 hours and there was no automated backup or disaster recovery.

The client needed an automated MLOps architecture on AWS to forecast crop yield and demand from seasonal, soil, fertilizer, and weather data. The system also had to recommend the optimal crops to plant per area and season to maximize yield.

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.

Content production was time-consuming and labor-intensive, with inconsistent quality that was hard to scale. Manual editing and review processes were error-prone and couldn't keep pace with publishing demands.

After a successful migration to AWS, the platform scaled fast but ungoverned — over-permissive IAM, no centralized compliance visibility, runaway costs, and environment drift left security reactive and enterprise audits nearly impossible.

The client's data pipeline was struggling with unmanaged data sources, scalability limits, and high data latency, while brittle error handling and heavy manual intervention made every incident a fire drill.

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 major financial services company needed to migrate their entire data center infrastructure to AWS while maintaining strict compliance requirements and zero downtime for critical payment processing systems.

The client's on-premises legacy infrastructure couldn't scale with a surging customer base, driving cost overruns, weak observability and security, and slow manual CI/CD. Peak-time scraping jobs degraded, SLAs slipped, and manual patching and provisioning drained engineering time.

Manual document processing was consuming significant staff time, with high error rates and inconsistent results across a large volume of varied document types.

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