Shaped is an AI-native personalization platform that enables businesses to rapidly implement and experiment with AI-driven search, recommendations, and personalization strategies. By analyzing real-time user behavior and content, Shaped delivers tailored experiences that enhance user engagement, increase conversion rates, and drive revenue growth. Its scalable and secure infrastructure ensures seamless integration with existing data sources, allowing companies to deploy personalized discovery experiences without the complexity of building in-house solutions.
Key Features and Functionality:
- Easy Set-Up: Shaped offers direct integration with existing data sources, enabling rapid deployment without the need for extensive infrastructure changes.
- Real-Time Adaptability: The platform ingests and processes user interactions instantly, allowing for dynamic re-ranking and personalization that adapts to evolving user preferences.
- State-of-the-Art Model Library: Shaped provides a comprehensive library of fine-tuned large language models (LLMs and neural ranking models, facilitating the creation of advanced recommendation systems.
- Highly Customizable: Businesses can build and experiment with various ranking and retrieval components tailored to their specific use cases, ensuring flexibility and relevance.
- Explainable Results: The platform offers in-session analytics and performance metrics, allowing users to visualize, evaluate, and interpret data effectively.
- Secure Infrastructure: Shaped's enterprise-grade security measures, including GDPR and SOC2 compliance, ensure data protection and scalability.
Primary Value and Problem Solved:
Shaped addresses the challenge of delivering personalized user experiences by providing a robust, AI-driven platform that integrates seamlessly with existing systems. It empowers businesses to enhance user engagement and satisfaction through real-time, relevant recommendations and search results. By offering a scalable and secure solution, Shaped eliminates the need for companies to develop complex in-house recommendation systems, thereby reducing development time and costs while achieving measurable improvements in user retention and revenue.
