Machine Learning & MLOps
Models that move a business number, and the MLOps that keeps them accurate long after launch.
Schedule a Free Strategy SessionWe identify the workflows where a model creates measurable value, such as lead scoring, customer segmentation, demand forecasting, and risk prediction, then build, validate, and deploy it into the systems your team already uses. Building the model is the easy part. We also set up the versioning, monitoring, and retraining that keep it reliable, so it does not quietly drift after the launch.
What You Get
- Use-case identification and ROI scoping
- Model development, validation, and A/B testing
- Integration with existing systems and APIs
- CI/CD for model training and deployment
- Model registry and version management
- Drift detection and automated alerting
Ideal For
Companies whose models never go live, or teams with live models that are drifting or hard to update.
Where This Already Runs
Tech Stack
Not Sure If This Is What You Need?
Book a free 30-minute session with a senior data architect. We'll tell you honestly whether this service fits your situation, and if not, what does.
Schedule Your Free Strategy SessionRelated Case Studies
Pond Score: Building a Machine Learning Risk Engine for Aquaculture Farms
ML-based pond health scoring model built inside AquaStackX — turning time-series water quality data into a daily 0–100 disease risk score with explainable alerts and a sub-60-second scoring pipeline.
91% Accuracy on 7-day disease risk prediction (back-test)AI-Powered Customer Segmentation Inside a Real Estate CRM: 38% Better Campaign Conversion
ML-powered customer segmentation built inside PropstackX — behavioral decay scoring, K-Means clustering with silhouette validation, real-time segment assignment via database triggers, and segment-driven campaign targeting.
38% Lift in campaign conversion rate through behavioral segmentationLead Intelligence Platform for Automotive Dealerships: 2.8x Improvement in Lead-to-Test-Drive Conversion
AI-powered lead scoring and prioritization system for a US automotive dealer network of 4,000+ dealerships — XGBoost classifier with sub-60-second scoring latency, SHAP-based explainability surfaced to salespeople, and a CRM priority queue that changed how agents work.
2.8x Improvement in lead-to-test-drive conversion for top-scored leadsLet's Find Your Starting Point
Book a complimentary 30-minute session with a senior data or AI architect. Bring your challenge, your stack, or an open question, and leave with a clear, honest assessment of where to begin.
No pitch. No obligation. Just a useful conversation.
Typically responds within 1 business day · Available globally