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Retail and E-commerce

Using ANAI, citizen data scientists and other developers with limited machine learning expertise can easily ingest data, generate insights and build/train ML models.

potential use cases

AI in Retail and E-commerce

Personalized Recommendations

Build AI-based recommendation engines for consumers to get product recommendations as around 91% of the consumers are more likely to place an order after getting a relevant recommendation.


Automate and optimize warehouses for better product movement and tracking to increase efficiency and ultimately improve the customer experience.

Demand Forecasting

Predict demands by training ML models on the past data and letting it forecast for the future through time series analysis leading to a better demand prediction and preparation for it.

Pricing Optimization

Optimize prices using past user data to give better deals to each and every user to increase user engagement and chances of the user placing an order.


challenges found

our platform

With ANAI, Build The Best AI Solutions

  • Easy-to-build and a quick experimentation and deployment platform to train, build and deploy models as quickly as possible using a no code UI approach.
  • Easily conduct monitoring and performance-related checks and detect any discrepancies and drops in the performance.
  • All-in-one solution for all your data science related works, reducing costs in hiring an entire team of ML professionals and enabling businesses to focus more on solving actual problems.
  • Enables citizen data scientists and developers to easily analyze and generate insights into the data to train models that create an impact.

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