Development of an AI-Powered Personalized Product Recommendation and Forecasting System

The client, operating in a complex financial environment, faces difficulties in delivering accurate, timely, and personalized product recommendations to over 50,000 customers across an inventory exceeding 100,000 financial products. Existing systems lack integration with external trends and real-time data, limiting ability to maximize sales opportunities and adapt to changing market conditions.

A mid-sized financial services provider seeking to enhance its personalized product offerings and demand forecasts for a large inventory of financial products to improve sales efficiency and customer engagement.

• Develop a scalable recommendation system capable of handling over 100,000 financial products and serving personalized suggestions to approximately 50,000 clients.
• Achieve a forecast accuracy of at least 88% in demand prediction to reduce stockouts and optimize inventory levels.
• Enable real-time updates and predictive insights to stay ahead of market trends and customer needs.
• Automate dynamic business offers based on customer demographics, behaviors, and external global events for increased personalization.
• Provide user-friendly querying and filtering functionalities based on natural language inputs and detailed customer/product profiles.
• Improve the overall effectiveness of business offers, leading to increased sales and customer satisfaction.

• High-capacity recommendation engine supporting over 100,000 products and 50,000 users.
• Retrieval-augmented generation (RAG) leveraging a vector database and large language models (LLMs) for relevant data contextualization.
• Sophisticated prompt engineering techniques, including Chain-of-Thought and Meta prompting, to enhance recommendation accuracy.
• Dynamic query system transforming natural language inputs into structured search queries, similar to ElasticSearch.
• Integration of external data sources, such as web search services, for real-time trend detection and contextual adjustments.
• Knowledge graph construction and dynamic population by LLMs to map customer preferences, product info, and interactions.
• Forecasting module with the aim of achieving at least 88% accuracy in demand predictions, reducing stockouts by around 0.9%.
• User interfaces for intuitive filtering and interaction, supporting natural language and structured queries.

• External web search services for real-time trend and event data
• Customer data sources and product databases
• Knowledge graph infrastructure for interconnected data mapping
• APIs for existing CRM or sales systems (if applicable)
• Real-time data streaming platforms for continuous updates

• Support for scalability to handle over 100,000 products and 50,000 customers
• Real-time data processing with updates and recommendations delivered within 1 minute
• Forecasting accuracy of at least 88%
• Stockout reduction target of approximately 0.9%
• High security standards for sensitive financial data
• Robustness, fault tolerance, and consistency in data handling

The implementation of this AI-powered recommendation and forecasting system aims to significantly enhance personalization and predictive accuracy, leading to increased sales opportunities and optimized inventory management. Expected outcomes include delivering tailored recommendations within one minute, achieving at least 88% demand forecast accuracy, and reducing stockouts by approximately 0.9%. Overall, the project will empower the client to stay ahead of market trends, improve customer engagement, and strengthen competitive advantage in the financial services industry.