Orca provides an online, memory-controlled deep learning platform for real-time adaptation of predictive and generative AI models. The system enables deterministic, low-latency evaluation of LLM and agentic AI flows using continuously updated, customer-specific criteria. This approach accelerates AI engineering cycles by allowing immediate model steering and customization without traditional retraining or feature engineering.
Funding
Funding not disclosed
Founders
Product
Problem
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.
Solution
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.
Target Audience
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.
Features
- 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