numbers in focus

"55% of sales leaders do not have high confidence in their forecasting accuracy" - Gartner

"67% of organizations lack a formalized approach to forecasting" - CSO Insights

Intelligent segmentation for lean models

Leveraging smart segmentation techniques grounded in time series characteristics, our platform clusters similar series together, reducing the necessity for numerous models.

Sieve/funnel methodology

Strategically sifts the data to identify optimal forecasting models, streamlining predictions for various scenarios.

Versatile models for diverse demand patterns

Provides capacity to employ a spectrum of models – from multi-time series to single-time series, zero-inflated, hierarchical, and more.

MLOps

Addresses large-scale forecasting challenges by providing a structured and automated approach to managing the entire ML lifecycle.

GenAI-based consumption

Consumes natural languages and provides a granular analysis of forecasts and scenario-based planning to arrive at the next best action. And nudges users with specific forecast details to make timely decisions.

Streamlined workflows

Revolutionizes engineering workflows, simplifying intricate processes like feature engineering, model selection, version control, and prediction pipelines.