Real Results for
Real Businesses

A mid-sized e-commerce retailer was overstocking seasonal inventory, tying up capital and increasing storage costs. We built a machine learning demand forecasting model trained on 3 years of sales data, seasonal trends, and external factors.

A healthcare provider had patient data siloed across 6 different systems, making reporting a manual, error-prone process. We implemented a lakehouse architecture that unified all data sources with real-time dashboards for clinical and operational insights.

A manufacturing company needed end-to-end visibility across a 50-supplier network to meet compliance requirements and prevent counterfeit parts. We built a Hyperledger Fabric consortium network connecting all suppliers with immutable provenance records.

A financial services firm was running a 15-year-old monolithic core system that couldn't scale with growth and required months for feature releases. We re-architected the platform into 12 microservices deployed on Kubernetes with full CI/CD.

A 3PL managing 500+ daily shipments was losing margins due to inefficient routing and manual planning. We implemented AI-powered route optimization and demand forecasting, reducing costs by 28% while improving on-time delivery to 96%.

Real estate agents using our AI-powered property recommendation system saw 45% more buyer engagement, 37% faster time-to-close, and 23% higher deal success rates.

A 200-bed regional hospital implemented AI-powered patient flow management and reduced ER wait times by 42%, improved bed utilization by 38%, and increased patient satisfaction by 35 points.

An e-commerce company deployed an AI chatbot that handled 68% of inquiries automatically, reduced support costs by 35%, improved response time from 4 hours to instant, and boosted customer satisfaction by 28 points.

A retail distributor implemented AI-powered warehouse automation, reducing manual picking errors by 75%, increasing throughput by 40%, and cutting labor costs by 22%.