No off-the-shelf tools. No generic solutions. We engineer custom AI systems — predictive models, LLM products, computer vision, automation — built to your exact requirements and delivered in 4–10 weeks.
From standalone ML models to full AI-powered products — built for production from day one
Full-stack AI products from idea to launch — LLM backends, vector search, multi-tenant architecture, billing
Custom machine learning models trained on your data — churn prediction, demand forecasting, fraud detection
End-to-end workflow automation powered by AI — document processing, data extraction, decision engines
Image and video AI — quality inspection, object detection, OCR, and visual analytics at scale
Build the data infrastructure that powers your AI — feature stores, ML pipelines, real-time serving
Embed AI capabilities into your existing products via clean, well-documented APIs and SDKs
Manual patient risk stratification taking 2 hours per clinician per day — delays in intervention
Custom ML model using 50+ clinical features. Built on AWS SageMaker, integrated with EHR via HL7 FHIR.
40% improvement in early risk detection. 25% reduction in preventable readmissions.
Caribbean food delivery platform losing $3M+ annually to fraudulent orders
Custom fraud detection model (XGBoost + Neural Net) with real-time scoring via SageMaker endpoints
77.8% live prediction accuracy. $3M+ in prevented chargebacks. 80% reduction in manual reviews.
Demand planning done manually in Excel — 40% forecast error causing stockouts and waste
Custom AI demand forecasting model with 50+ feature inputs, integrated with ERP for automatic PO generation
50% better forecast accuracy. $2M annual savings in inventory costs.
We cover the full breadth of modern AI/ML — not just LLMs
We work with you to define the exact AI problem, success metrics, and data requirements before writing a line of code
Technology selection, data pipeline design, model architecture, and integration plan — all documented before build starts
Agile development with weekly demos. Model training, evaluation, and iteration until accuracy targets are met
Production deployment with monitoring, documentation, and optional ongoing support or team training
No discovery calls that go nowhere. We'll review your use case, tell you if it's viable, scope it, and start building.