AI / ML & Optimization
We incorporate years of successful enterprise deployments in the world's most advanced predictive solutions into our analytic component & methods (ACMs). The ACMs are the core elements of our solutions and SaaS encapsulating the machine learning and optimization algorithms that enable cloud native, enterprise quality science-based solutions.
Our goal: Apply AI & Machine Learning to make applications more intelligent for all organizations
Our solutions are value focused with speed to delivery and sustainability as priorities. We provide the math behind intelligent applications, along with the services you need, to infuse your existing applications or create new ones powered by AI/ML quickly and cost efficiently. Our Analytics as a Service makes AI/ML economical and sustainable at a low cost of ownership.

Pre-built Machine Learning Engines tuned to your data

Enterprise Forecasting
Our Forecast ACM is a containerized microservice packed with features, developed from our extensive experience implementing enterprise forecasting across many domains. We have a library of several algorithms wrapped in auto-ML to pick the best model for any series. We also have specific technology to incorporate advanced sales or other leading indicators not found in other packages.

NLP & Feature Extraction
We have tried and true microservice components that can process documents and web pages, extract features, and identify patterns.

Segmentation & Clustering
We leverage supervised and unsupervised methods to segment and group customers and products to create new insights and support other ML models. Applications include: Customer-Product Segmentation, Churn Prediction and (Next Best) Product Recommendation.

Price Elasticity
Our pricinples pioneered estimating customer response to price and other attributes in many industries. Our ML based methods have the flexibility to incorporate competive prices, alternative products, bundling, and a host of customer and product attributes for both B2B and B2C applications
