Real transformations, real results

The Challenge

A mid-size fintech processing 2M+ daily transactions was running on a 12-year-old Oracle data warehouse. Nightly ETL jobs took 9 hours. The analytics team waited 2 days for reports. AI initiatives were stalled because the data couldn't be accessed in formats ML tools required.

Our Approach

• Architecture assessment and migration roadmap design
• Apache Iceberg-based lakehouse on cloud-native infrastructure
• Parallel-run validation with zero-downtime cutover strategy
• Real-time streaming layer (Kafka → Flink → Iceberg) for transaction data
• dbt-based transformation layer replacing 400+ stored procedures

The Impact

The analytics team went from 2-day report waits to real-time dashboards. The ML team deployed their first production model within 6 weeks of migration completion. The CTO called it 'the best infrastructure investment we've made in a decade.'

The Challenge

A large manufacturing enterprise had a 15-person central data team serving 8 business domains. Ticket backlog exceeded 6 months. Every new analytics request required the central team's involvement, creating friction between business units and data engineering.

Our Approach

• Domain decomposition workshop — identified 8 data product domains
• Self-serve data platform design with infrastructure-as-a-platform
• Data product API and discovery portal implementation
• Federated governance framework with data contracts and SLAs
• Phased rollout: 2 pilot domains → full mesh over 6 months

The Impact

Business domains now own their data products end-to-end. The central team shifted from bottleneck to platform enabler. Two domains built their own ML models without any central team involvement — something previously considered impossible.

The Challenge

A pharmaceutical research company was accelerating AI-driven drug discovery but couldn't meet data sovereignty requirements across 3 jurisdictions (EU, India, US). Patient data governance was manual, audit preparation took weeks, and the risk of non-compliance with GDPR and upcoming EU AI Act was growing.

Our Approach

• Data sovereignty assessment across all data flows and storage locations
• Federated governance framework with automated policy enforcement
• Data contract implementation between research, clinical, and analytics domains
• Automated data lineage and catalog deployment for audit readiness
• AI model governance layer: bias detection, explainability, approval workflows

The Impact

The company passed its next GDPR audit with zero findings — a first. Researchers gained self-serve access to compliant datasets, accelerating the drug discovery pipeline. The governance framework became a competitive advantage in partnerships with EU-based research institutions.

The Challenge

A major e-commerce company wanted to move from rule-based product recommendations to AI-driven personalization. Their existing platform couldn't serve features in real-time, training data was inconsistent with serving data, and deploying a new model took 3+ months end-to-end.

Our Approach

• Feature platform architecture design (online + offline serving)
• Vector database deployment for embedding-based similarity search
• RAG pipeline for AI-powered product discovery and search
• Real-time feature computation with Kafka Streams and Flink
• MLOps pipeline: automated training, validation, A/B testing, and deployment

The Impact

Personalization revenue increased by 31% in the first quarter post-launch. The ML team went from deploying 1 model per quarter to shipping 12 in production simultaneously. The platform became the foundation for their generative AI product search feature.