DEMAND
FORECASTING
PLATFORM
Experience the leading AI demand forecasting platform.
From small E-commerce stores to large online retailers this platform will support your demand forecasting and demand planning.
WHY REMI
Best-in-Class Demand forecasting.
Remi Ai’s Demand Forecasting API is an AutoML deep learning API service that utilises best-in-class algorithmic approaches for you supply chain and sales forecasting.
Unlike traditional demand planning that relies mostly on consumption and historical sales to build a forecast, Remi Ai encompasses external data streams such as promotional behaviour, weather and global events to more accurately predict future time series.
Inbound Data
Solve your forecasting challenges faster with rapid onboarding.
With native integrations built for many data sources, Remi can rapidly connect to your internal systems to extract the data required to produce your machine learning forecasts.
Probabilistic
Forecasts
Remi AI’s probabilistic forecasts capture the possible breadth of demand, rather than just the average forecast. Giving you confidence in the face of uncertainty in your Supply Chain.
70%
Reduction in
back orders
15%
Increase in
cash flow
14
Demand
Forecasting Methods
AUTOML
Behind our Demand Forecasting APIs are 14 different time series methods, including recurrent neural networks, deep learning, probabilistic, ARIMA and other models to choose from.
But you don’t need to know anything about these models, instead our AutoML service handles all the training, optimisation, testing of the 14 models, so you know you’re guaranteed the best model for your specific data.
This model and its parameters are saved for each of your products, ready to deliver forecasts as new data becomes available.
14
Different demand forecasting methods
STATE OF THE ART MACHINE LEARNING
Using best in class probabilistic and deep learning time series models, our APIs hit state of the art of benchmarks, which are up to 50% more accurate than traditional time series methods.
50%
More accurate
Some of our clients
