Area Two · AI Frameworks
AI frameworks built on mathematical foundations. Edge-ready, deterministic, and composable from primitives.
One of two areas built on a shared mathematical foundation.
What we build
Most modern AI gets built through long iteration on hyperparameters and architectures. Our frameworks lean on mathematical structure that tells you something useful about what an architecture will do before you train it.
Run capable language models on resource-constrained devices. Local-first inference without cloud round-trips.
On-device inference
Compression guided by mathematical structure rather than trial-and-error pruning. Smaller models that keep their reasoning capability.
Model optimization
Identical inputs produce identical outputs. The prerequisite for testing, certification, and trustworthy deployment.
Reproducibility
Building intelligence from simpler, proven components, closer to LEGO blocks than to black boxes. Transparent and debuggable.
Architecture
Networks structured so that most parameters don't fire on most inputs. Efficiency as a property of the model, not a post-hoc optimization.
Architecture
Predictable response to bounded inputs. This is the foundation for certifying AI in domains that need more than empirical confidence.
High-reliability AI
The technical approach
The same idea that drives compressed sensing in signals shows up in neural networks. For most inputs, most weights contribute very little. Architectures that take this into account end up smaller and easier to interpret.
Weight matrix cycling between dense and sparse. Most weights, on most inputs, contribute very little.
Why principled AI
Most modern AI is built through long iteration on hyperparameters and architectures. That works. It also runs out of room. Each tweak is local, expensive to evaluate, and easy to lose track of. The improvements rarely transfer.
Tying improvements back to mathematical structure tends to compound. The same insight that helps one architecture often helps the next one, and the analysis that justifies it carries over with it. That's the part of the work we focus on.
Predictable
What works in evaluation works in production.
Edge-ready
Built for the device, not squeezed onto it.
Transparent
Compositional behavior, easier to audit.
Certifiable
Tested the way other software is tested.
The other area
Our AI work grew out of our signal processing work. Both areas draw from the same mathematics, and the signal side is where it first proved out for us.
Edge constraints, certification requirements, or research collaborations.
Contact Us