When the right answer is a model, not a prompt
Not every problem is a generative AI problem. Sometimes the right answer is a focused, supervised model: small, fast, cheap to run, and dramatically more accurate than throwing the question at a foundation model.
We build and deploy ML systems for prediction, classification, ranking, anomaly detection, and forecasting. And we wire them into the products and workflows where the predictions actually get used.
Capabilities
- Tabular ML: gradient boosting, scikit-learn, fast iteration on structured data.
- Time-series forecasting for demand, capacity, financial metrics.
- Anomaly detection for fraud, fault, and operational signal.
- Computer vision: defect detection, OCR, object identification.
- NLP beyond LLMs: classification, named entity recognition, intent detection.
- MLOps: model versioning, monitoring, retraining pipelines on AWS SageMaker, Vertex AI, or your own stack.
