AI is becoming a one of the main drivers of shopping in retail, e-commerce and consumer goods. Machine learning gives businesses a strong competitive advantage and is available to businesses of any size or budget.


Next, we list our solutions for retail, e-commerce & consumer goods enterprises.


  • Reduction of inventory levels by 10%-30%.
  • Cost savings.

Intelligent Inventory Management

For staying competitive in today’s world, organizations need to constantly reconfigure supply chains and continually manage the changing demand and supply. Intelligent decisions in inventory management can drive large cost saves and avoid missing selling opportunities.


  • Increase revenue between 10%-35%.
  • Improve customer experience.
  • Increase upselling opportunities.

Personalized Recommendations

A recommendation system uses machine learning to understand the customers preferences with the objective of recommending them products. In e-commerce, retail and consumer goods, personalized product recommendations are key to run a profitable business.


  • Increase revenue between 10%-45%.
  • Improve customer experience.
  • Increase upselling opportunities.

Visual Recommendations

In the fashion and luxury retail industries, recommending products based on just traditional features like brand or price might not be enough. The visual appearance plays an important role on what the customer decides to buy. Machine learning can be used to analyze a garment or a luxury piece to extract visual information and provide a more meaningful recommendation.


  • Increase CTR between 15%-65%.
  • Increase conversion between 10%-35%.
  • Deliver personalized experiences.

Website Optimisation

Optimising a web using AI can dramatically boost Click-Through Rate (CTR) and conversion. The system monitors the performance analytics of a website to discover insights and automatically understands the audience preferences. Using A/B or multivariate testing, different contents are generated to segmented users for improving traffic and revenue.


  • Increase sales forecast accuracy between 35%-75%.
  • Reduce labor costs.
  • Improve market understanding.

Sales forecasting

Traditional sales forecasting is difficult, time-consuming and incorporates human bias in the process. AI-enabled forecasting tools uses data mining, statistics and machine learning to analyze previous wins, performance patterns and other historical data. This technique can calculate the probability of winning deals, predict expected revenue and help you create more accurate sales forecasts.


  • Increase revenue between 15%-45%.
  • Improve customer satisfaction.
  • Increase CTR between 25%-40%

Price Optimisation

Traditional static pricing keeps prices absolute, dynamic pricing, on the contrary, adjusts prices in real-time based on external factors and customers individual buying habits. Dynamic pricing solutions use machine learning and big data to find customer’s data patterns, aggregate prices from competitors, analyze consumption in different regions and predict customer behavior over promotions and loyalty programs.


  • Reduce fraud between 45%-95%.
  • Reduce costs.
  • Improve customer satisfaction.

Fraudulent Product Detection

Multi-channel e-commerces and specially sales aggregators are particularly susceptible to fraud. AI reduces fraud by applying real-time continuous learning and by intelligently flagging transactions that appear suspicious, identifying falsified products and detecting fraudulent merchants.

Do you have a use case for your industry that is not listed here? Send us an email to