Real solutions. Real results. Explore our recent work.
A Fortune 500 retail company needed real-time insights from 50+ data sources but was drowning in spreadsheets and manual reporting, causing delayed decisions.
Built a custom AI analytics platform with automated data ingestion, ML-powered anomaly detection, and interactive dashboards processing 1M+ data points daily.
85% reduction in reporting time, 40% improvement in inventory forecasting accuracy, $2.3M annual savings from optimized operations.
Legacy e-commerce platform built on outdated technology couldn't handle growing traffic, leading to frequent downtime during peak sales and poor mobile experience.
Complete platform rebuild using modern microservices architecture with React storefront, Node.js backend, and cloud-native infrastructure with auto-scaling.
99.9% uptime during Black Friday (10x previous traffic), 60% faster page loads, 35% increase in mobile conversion rates.
Enterprise teams spent 3+ hours daily on repetitive browser tasks -- copying data between tabs, filling forms, and managing multiple SaaS tool workflows.
Developed a suite of 5 Chrome extensions with shared authentication, cross-tab communication, and deep API integrations with Salesforce, HubSpot, and Slack.
50K+ active users, 2.5 hours saved per user daily, adopted by 200+ enterprise teams, 4.8★ Chrome Web Store rating.
Financial services firm needed to process millions of transactions in real-time for fraud detection, but existing batch processing had 6-hour delays.
Architected a real-time streaming data pipeline with Apache Kafka, custom ML fraud detection models, and automated alerting system with sub-second latency.
Processing 5M+ transactions/day in real-time, 94% fraud detection rate (up from 67%), $4.1M prevented in fraudulent transactions in first quarter.
Startup needed to launch an AI-powered project management SaaS within 4 months to meet investor milestones, with zero technical team in place.
End-to-end development of a multi-tenant SaaS platform with AI task prioritization, resource allocation optimization, and real-time collaboration features.
Launched on schedule, acquired 1,200 paying customers in 6 months, secured Series A funding of $3.5M based on product traction.
Media streaming platform with 2M+ users had a generic recommendation system causing high churn -- users couldn't discover relevant content efficiently.
Built a hybrid recommendation engine combining collaborative filtering, content-based analysis, and deep learning models trained on user behavior patterns.
45% increase in content engagement, 28% reduction in churn rate, 3.2x improvement in content discovery, recommendation accuracy of 89%.
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