Leading Research & Content Platform
Delivered AWS cloud data pipeline for 70M+ records with 1M+ queries per day across 50+ microservices. Designed ML automation pipeline for content classification reducing publishing cycle time by 15%. Managed multinational team of 70+ engineers.
Challenge
A leading research platform had 70M+ records spread across monolithic databases designed in the early 2000s. Query performance degraded as data grew. Content classification was manual — PhD-level editors spending hours categorizing papers that ML could handle in seconds. The engineering organization of 70+ was running on waterfall with no CI/CD.
Approach
Led a multi-year transformation: 1. Decomposed monolith into 50+ microservices on AWS. 2. Built ML classification pipeline — trained models on historical editor decisions to auto-classify new content with 94% accuracy. 3. Introduced SAFe across the 70+ person engineering organization. 4. Designed data lake architecture for 70M+ records with sub-second query performance. 5. Implemented CI/CD pipeline reducing deployment time from days to hours.
Outcome
70M+ records migrated to modern data architecture. 1M+ queries served daily with <200ms P99 latency. ML classification reduced publishing cycle time by 15%. SAFe adoption improved cross-team coordination and predictability. The platform is now the foundation for next-gen AI search features.
Architecture
Interactive Demo
Technology Stack
Book Your AI Consultation
Start with a free consultation. We'll assess your AI readiness, identify high-impact opportunities, and scope a concrete first engagement.