Circly provides a machine learning-based demand forecasting platform specifically designed for the retail sector, enhancing supply chain efficiency by accurately predicting sales trends. By integrating with existing systems through a modern API, Circly enables businesses to optimize inventory management and reduce resource waste without extensive retraining or system overhauls.
Funding
$380K 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
Retailers and manufacturers often struggle with inaccurate demand forecasting, leading to overstocking, waste, and inefficient supply chains. Traditional forecasting methods often fail to account for numerous internal and external factors, resulting in suboptimal inventory management and lost revenue opportunities.
Solution
Circly offers a machine-learning-based demand forecasting platform designed to improve supply chain efficiency for production and trade companies. The platform integrates with existing enterprise systems via API, enhancing planning accuracy without requiring extensive system overhauls or employee retraining. Circly's AI models analyze historical sales data, promotions, weather patterns, and other relevant variables to generate precise demand predictions. These forecasts enable businesses to optimize inventory levels, reduce waste, and make data-driven decisions regarding assortment and resource allocation.
Target Audience
Circly primarily targets production and trade companies, including food and beverage manufacturers, retailers, and distributors, seeking to optimize their supply chain and reduce waste through improved demand forecasting.
Features
- AI-powered forecasting models specialized for production and trade companies
- Integration with existing ERP and supply chain management systems via API
- User-friendly dashboards accessible on various devices for real-time insights
- ABC analysis at the article level and visitor frequency display for retailers
- Ability to filter and identify underperforming articles to optimize assortment
- In-house deep-learning servers located in the EU for data security
- Models incorporate both internal sales data and external factors like holidays and weather