Veer Consulting — Selected Case Studies

Small and mid-sized businesses struggle to adopt AI due to fragmented information, unclear ROI, and lack of contextual guidance tailored to their industry and constraints.

A production WordPress application on Elastic Beanstalk faced scaling constraints, slow deployments, and limited operational control. The team needed a reliable, repeatable deployment model on Kubernetes while minimizing downtime and risk.

Legacy Jenkins pipelines were slow, brittle, and expensive to maintain. The team needed faster feedback cycles, simpler maintenance, and tighter integration with code reviews and branch protections.

A fintech team relied heavily on manual reconciliation processes, leading to operational inefficiencies, delayed reporting, and engineering bottlenecks in deployment.

Training computer vision models required large volumes of labeled data, which was costly, slow to acquire, and difficult to generate consistently across environments.

As AI agent systems grow more complex, teams lack visibility into agent reasoning quality, failures, and experience-level metrics.

Managing fleets of industrial devices required reliable offline support, real-time control, and secure cloud orchestration.

Organizations needed standardized ways to validate AI systems for safety, robustness, fairness, and governance compliance.