Every project has a success metric. Every model has a production story. Here's the data.

Client: Tier-1 Automotive Parts Manufacturer
Problem: Manual quality inspection was catching only 82% of surface defects — costing $2.3M/year in warranty claims.
Solution: Custom object detection model deployed on edge hardware at 6 inspection stations. Real-time inference with automated rejection.
Client: Am Law 100 Firm
Problem: Contract review consumed 4 hours per document — lawyers were bottlenecked on repetitive clause analysis.
Solution: NLP pipeline for automated clause extraction, risk scoring, and anomaly detection. Integrated into existing document management system.


Client: Regional Fashion Retailer
Problem: Generic "customers also bought" recommendations were underperforming — low engagement, no personalization.
Solution: Hybrid collaborative filtering + content-based model serving 2M+ daily users. Real-time feature pipeline with A/B testing framework.
| Model | Domain | Key Metric | Score | Status |
|---|---|---|---|---|
| vision-v3.2 | Manufacturing QA | Accuracy | 97.4% | PRODUCTION |
| nlp-v2.1 | Legal Document Review | F1 Score | 0.94 | PRODUCTION |
| fraud-v4.0 | Financial Services | AUC-ROC | 0.987 | BETA |
| rec-engine | E-Commerce | NDCG@10 | 0.89 | PRODUCTION |
| ocr-v2.0 | Healthcare Records | CER | 1.2% | PRODUCTION |
| anomaly-v1.3 | Network Security | Precision | 96.1% | PRODUCTION |
| speech-v3.0 | Customer Service | WER | 4.8% | TRAINING |
| forecast-v1.2 | Supply Chain | MAPE | 6.3% | PRODUCTION |
From prototype to production — with measurable outcomes at every stage.