🤖 Primex Labs
How an AI-native fintech company built a production-grade ML platform, serving 500+ models with sub-millisecond feature delivery and comprehensive model governance.
Company Overview
Primex Labs is an AI-first fintech company that provides credit decisioning, fraud detection, and risk assessment services to 200+ financial institutions. Their platform processes 50 million decisions daily, powered by hundreds of machine learning models.
The Challenge
Primex's data science teams faced significant obstacles in scaling their ML operations:
- Feature engineering bottleneck — Data scientists spent 70% of their time on feature preparation
- Training-serving skew — Models performed differently in production than in training
- Limited observability — Couldn't detect model drift until business metrics degraded
- Governance gaps — Regulators required explainability that existing infrastructure couldn't provide
The Solution
Primex deployed Datorth's AI-ready data platform to build a world-class ML infrastructure:
Unified Feature Store
- Built centralized feature repository with 5,000+ production features
- Implemented point-in-time correct feature retrieval for training
- Deployed real-time feature serving with <5ms p99 latency
- Created feature marketplace for cross-team discovery and reuse
ML Model Telemetry
- Implemented comprehensive model monitoring across 500+ production models
- Built automated drift detection with configurable alerting thresholds
- Created A/B testing framework with statistical significance tracking
- Deployed model performance dashboards with business metric correlation
AI Governance Framework
- Automated model documentation and lineage tracking
- Built explainability pipelines for regulatory compliance
- Implemented bias detection and fairness monitoring
- Created approval workflows for model promotion to production
Results
Primex achieved transformative improvements in their ML operations:
Data Science Productivity
- 70% → 20% time spent on feature engineering
- 3× faster model development cycle
- 80% feature reuse rate across teams
- 50+ models/month deployment velocity (up from 5)
ML Operations Excellence
- <5ms p99 feature serving latency
- Zero training-serving skew incidents
- 15 minute mean-time-to-detection for model drift
- 100% model explainability for regulatory audits
Business Impact
- 23% improvement in credit decision accuracy
- $31M reduction in fraud losses for customers
- 40% faster regulatory approval for new models
- 5 new enterprise customers citing AI capabilities
Customer Testimonial
"Datorth transformed our ML infrastructure from a science project into an industrial-grade platform. Our data scientists now focus on model innovation instead of data plumbing, and we can prove to regulators exactly how our models work."
— Dr. James Park, VP of Machine Learning, Primex Labs
Technology Stack
- Datorth Feature Store with real-time and batch serving
- Datorth ML Observability for model monitoring
- Datorth Governance for AI compliance
- Integration with Databricks, MLflow, Kubernetes, and custom training infrastructure
ML Platform Capabilities
- Feature Engineering — Batch and streaming feature pipelines
- Feature Serving — Low-latency online inference
- Model Registry — Versioned model artifacts with metadata
- Model Monitoring — Drift detection, performance tracking
- Explainability — SHAP values, feature importance, decision traces
What's Next
Primex is expanding their Datorth deployment to support large language models (LLMs) for document processing and conversational AI, with a focus on retrieval-augmented generation (RAG) pipelines.
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