Razorthink Foresight is a demand forecasting and planning software that connects to various data sources, enabling businesses to create targeted models for accurate predictions. It addresses the challenge of outdated forecasting systems by automating data cleansing, model selection, and scenario management, allowing organizations to optimize inventory and improve decision-making within a week.
Funding
$15M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Many businesses struggle with outdated demand forecasting systems that fail to adapt to rapidly changing market conditions and complex supply chains. These systems often rely on manual data cleansing, generic models, and limited scenario planning, leading to inaccurate predictions and suboptimal inventory management.
Solution
Razorthink Foresight is a demand forecasting and planning software designed to adapt to unique business needs by connecting to virtually any data source. It creates targeted models for accurate predictions and automates data cleansing, model selection, and scenario management. The platform enables businesses to incorporate multiple signals into forecasts, including macroeconomic data, competitor information, and market changes, while also allowing for user inputs and manual adjustments. Razorthink Foresight helps organizations optimize inventory, automate purchase orders, and improve decision-making.
Target Audience
The primary customers are businesses across various industries looking to improve their demand forecasting accuracy, optimize inventory levels, and enhance supply chain planning.
Features
- Connects to various data sources, including ERP systems, channel APIs, demographic data, and social media data.
- Accepts data in any format, including structured and streaming data, with tools to rapidly map attributes to target schemas.
- Automates the creation of forecasting datasets by combining dimensional and operational data into multi-attribute time series.
- Cleanses data and imputes missing values automatically using built-in tools.
- Automates model selection based on multiple criteria, targeting appropriate algorithms to each circumstance.
- Uses meta models to integrate multiple dependent forecasts for improved prediction accuracy.
- Provides new product forecasts using techniques such as similarity, lineages, and segmentation from preferences.
- Offers conversational AI for natural language-driven inquiries about product characteristics, forecasts, and scenarios.
- Manages multiple forecast scenarios, allowing comparison against a baseline and evaluation of promotions, pricing events, and product introductions.