Production-grade ML, not magic — versioned, observable, retrainable.
Targeted AI deployments where the input data, the model, and the inference path all stay inside your infrastructure. Predictive demand, document intelligence, anomaly detection, and decision-automation — built with deliberate scope and measurable success criteria.
Predictive analytics — demand forecasting, churn, lead scoring Document intelligence — OCR, classification, structured extraction Anomaly detection on telemetry, transactions, and logs Computer vision for inspection and counting workflows Decision automation with human-in-the-loop fallbacks Model versioning, drift monitoring, and rollback paths Use-case scoping — define accuracy targets and business KPIs upfront Data audit — assess quality, coverage, and labeling pipeline Baseline model first — beat a simple rule before adding complexity Shadow deployment — run alongside humans before automating decisions Continuous monitoring — drift, accuracy, and downstream business metricsAI works in production when it's treated as engineering. Every model we deploy ships with a baseline, a drift monitor, an explanation surface, and a fallback path. You get measurable lift on a defined business metric — not a demo that impresses in a sandbox and silently degrades a quarter later.