Practices used
AWS services involved
  • Amazon API Gateway
  • Amazon Bedrock
  • Amazon Bedrock
  • Amazon S3
  • Amazon Simple Queue Service (SQS)
  • Artificial Intelligence
  • AWS Cloudwatch
  • AWS Lambda
  • Generative AI
Gen-AI-powered next-generation Planner is an online service designed for daily and/or weekly meal planning and delivery for businesses and families. For the business-oriented service, beSharp has developed a Generative AI-based advanced planning and recommendation engine. The engine autonomously suggests and plans meals based on individual preferences, starting from a textual prompt the user provides in natural language.


The goal of was twofold: on the one hand, to increase customer retention and loyalty while reducing friction; on the other hand, to minimize the risk of loss in earnings. Specifically:

  • Eliminate the risk of users forgetting to place lunch orders in time, especially on Fridays for Mondays’ meals.
  • Reduce the drop rate caused by the time required to browse the entire menu and select dishes.
  • Enhance the user experience when choosing and planning meals.


beSharp has developed a next-generation daily and/or weekly planner based on Generative AI using Amazon Bedrock. LLMs have paved the way for a completely new user experience that is far better than classical recommendation engines’ experience based on NLP algorithms, best fit, and recommendation. The solution includes:

  • Possibility for users to use natural language to express their food preference criteria.
  • Independence from labeling, collaborative filtering, and large amounts of historical data.
  • Implementation of a meta-prompt that can provide the system with details that are essential for the solution to work, including the entire menu available on the platform. This part remains invisible to the users, ensuring the perfect balance between UX and cost control.


  • Natural language
    Highest level of customization and accessible UX
  • 100% fitting results
    User expectations are fulfilled thanks to the criteria provided through the prompt
  • Time-saving
    The solution helps save time by reducing decision-making time. This increases the conversion rate.
  • Minimal implementation effort
    Through LLM, tasks such as model creation, selection, and training can be bypassed. This minimizes time-to-market and offers a real competitive advantage.
  • Maximization of company earnings
    The planner ensures daily earnings, which are always optimized since the model plans the meals using the maximum number of credits possible, according to the 80 credits/day available.
  • Zero waste
    Thanks to targeted recommendations, no dish is wasted.


  • Order time reduced from 15 minutes to 2 minutes.
  • User spending is reduced by up to 45% compared to having lunch in a restaurant or ordering from other services based on the same food.


  • Accessibility has always been committed to accessibility. Therefore, a vocal prompt will be available soon.
  • Wider service availability
    By the end of the year, the mobile app feature will be released, and the Gen-AI-based service will also be available for families.
  • Continuous Improvement
    The company aims to improve output quality through techniques like RAG (Retrieval-Augmented Generation) that allow LLMs to leverage additional data without the need for model retraining.


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