Build AI models that arecheaper and more capable.
FDRA is a more efficient architecture for AI systems. We train bespoke models for text, audio, video, and physical simulation at a fraction of the cost.
One architecture, multiple modalities.
We build custom AI models across text, audio, video, and physical simulation using our more efficient architecture.
Text
Language models for generation, reasoning, and understanding with extended context capabilities.
Audio
Speech and audio models for recognition, synthesis, and understanding applications.
Video
Vision and video models for analysis, generation, and multimodal understanding.
Physical Simulation
Models for physics, robotics, and scientific computing applications.
Need a custom model trained on your data? We build bespoke solutions.
Talk to UsA new approach to sequence modeling.
Traditional transformers face quadratic memory scaling with sequence length. FDRA achieves constant memory through a physics-inspired design.
Constant Memory
O(1) memory cost regardless of sequence length, compared to O(n²) for standard self-attention.
Extended Context
Process sequences far beyond the backbone's native window. Tested on sequences up to 49,000+ tokens.
Frozen Compatible
Works as an adapter for pretrained transformer backbones without requiring retraining of the base model.
Preliminary Results
Long scientific articles (up to 49K tokens, backbone window 8K)
| Setting | Validation Loss | Perplexity |
|---|---|---|
| Baseline (Reset) | 3.50 | 33.1 |
| FDRA (With Carry) | 2.80 | 16.4 |
Evaluated on 271 long scientific articles. FDRA enables cross-attendable memory from sequences 6× longer than the backbone's native context window.
A more efficient architecture for AI.
Traditional AI architectures hit fundamental limits in memory and compute. FDRA is a ground-up redesign that achieves constant memory scaling, enabling models that are both cheaper to train and more capable.
We train bespoke models on your data using our proprietary architecture. The result: state-of-the-art performance at a fraction of the cost.
Constant Memory
O(1) memory regardless of context length, vs quadratic scaling in traditional models.
Extended Context
Process sequences far beyond typical limits. We've tested on 49,000+ token sequences.
Bespoke Training
Custom models trained on your data for your specific use case.
Get started with FDRA
Ready to build with the most efficient foundation models? Talk to our team.