Traditional AI/ML models struggle to adapt to evolving data, shifting business objectives, and new use cases, often requiring costly and time-consuming retraining. This inflexibility leads to performance degradation, missed opportunities, and increased maintenance complexity.

Orca provides a memory-augmented MLOps platform that enables real-time adaptation of AI models without retraining. By separating a model's reasoning from its knowledge, Orca allows users to update the model's behavior by modifying external data stored in a dynamic memory dataset. This approach allows models to instantly adjust to changing data distributions, new business priorities, and diverse user preferences. Orca's platform integrates data management, models, and automated tuning, transforming models into retrieval-augmented systems that adapt instantly to change.

Memory-augmented architecture that separates reasoning from knowledge, enabling real-time updates without retraining

Dynamic memory dataset that stores and manages external data used to update model behavior

Retrieval-augmented classification (RAC) models that retrieve relevant memories to guide predictions

Memory-mixture-of-expert (MMOE) approach that matches state-of-the-art classifier performance

Cross-attention mechanism that learns how to weigh memories based on input

Ability to trace data responsible for any given prediction, enabling targeted data optimization

Support for various model types, including classification models, image classifiers, and sentiment analysis models

Integrates with existing ML workflows and infrastructure

Orca is designed for machine learning teams and data scientists who need to build and maintain adaptable AI models that can respond to changing data, business objectives, and user preferences.