Case Studies

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.

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.

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.

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.

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.

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.

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