OneModel brings foundation models to production.
OneModel uses a bespoke algorithm for integrating diverse types of task behaviour and reasoning under one common language. It subsequently learns on the job through (safe) experimentation.
Core technology
OneModel is a unique AI orchestrator that creates a growing, reusable skills base that applies across many tasks.
- Segregates tasks into reusable, transferable mini-capabilities
- Each is a piece of discrete explainable and transparent “reasoning”.
- Capabilities can be added/removed piecemeal.
Adaptable
Accommodates data drift without reinforcement learning's high data needs and without catastrophic forgetting.
Useful from day one
OneModel inverts the foundation model approach by deploying a small, robust application, taught on very little data, and incrementally building out new capabilities.
Transfer learning
Any edge-cases handled, or new abilities added on one machine are transferred piecemeal to other machines.
Safe learning
Real-world testing is dangerous. OneModel provides effective Sim 2 Real which means less real-world testing needed.
Multi-robot
OneModel can adapt to new sensors and actions/robot arms without retraining. Segregated learning combined with the boolean inputs means that new IO does not affect prior learning.
Segregated inference means low GPU/compute requirements.
Segregated inference also means OneModel easily excludes PII by design - we don't keep data, we keep capabilities.
OneModel does not depend on existing frontier models, nor on public domain ones. All of its data is proprietary.
OneModel uses one-shot (and zero-shot) learning. This means significantly less data is needed to learn new capabilities.