Developing generative AI applications requires managing sensitive user data while ensuring relevant and personalized user experiences. Current methods struggle to balance data privacy with the need for effective contextualization and fast processing.

FutureAI's Masterpiece is a generative application architecture designed for secure and efficient data processing, enabling developers to create personalized user interfaces. The platform employs a dual dataset framework that filters sensitive user data before contextualization, addressing privacy concerns. Masterpiece utilizes a pipeline to extract sensitive user data, vectorize the remaining data, and ingest it for contextualization. This process pairs user data with application data to generate relevant content within 5,000ms. Developers can select from various versions of the Leo 1 model, including a base model fine-tuned for contextualization, a LoRa-adapted version, or a fully fine-tuned model for optimal performance.

Dual dataset architecture for privacy and security

User Data Pipeline that extracts sensitive data categories such as toxic, derogatory, violent, sexual, and financial information

Vectorization and ingestion of user data for contextualization

Integration with Leo 1 models, including Base, LoRa-trained, and Fine-tuned versions

SDK for prompting users and connecting their data to FutureAI

Text and multi-modal embeddings for data representation

Clustering and pairing of user and developer data

Rapid processing within 5,000ms from data ingestion to interface generation

The primary users are developers looking to build generative AI applications that require secure and efficient data processing while providing personalized user experiences.