Real systems.
Real results.

The Challenge

A mid-market fintech was running 12 models in production — all deployed manually from Jupyter notebooks. No versioning, no rollback, no monitoring. One bad model update caused a 3-day outage in their fraud detection pipeline.

Our Approach

• Audited all 12 model pipelines and their data dependencies
• Implemented model registry with version tracking and eval baselines
• Built CI/CD pipeline with automated evaluation gates
• Set up real-time monitoring with drift detection and alerting

The Challenge

A Series B healthtech company wanted to ship an AI-powered clinical decision support tool. But their data infrastructure was a patchwork of CSV exports, cron jobs, and a single overloaded Postgres instance. They were 6-12 months away from production AI — and didn't know it.

Our Approach

• Mapped all data sources and identified quality gaps
• Designed and implemented a modern lakehouse architecture
• Built automated data quality checks with alerting
• Created feature store for ML-ready data access