We build language models that run anywhere — locally, privately, at the speed of thought. Lambert is our first.
Our flagship model
The first sub-billion parameter model built for real-world edge deployment. No cloud. No latency. Just intelligence, wherever you need it.
What makes neuromorphic chips different from GPUs?
Neuromorphic chips process through asynchronous spike events rather than dense matrix operations — activating only when something changes, just like biological neurons.
Research
SNNs process through sparse, event-driven spikes — up to 100× more efficient than dense transformers for suitable workloads.
Custom silicon designed natively for SNN workloads. No GPU overhead — hardware that mirrors the brain's own architecture.
Distillation, quantization, and architecture search squeeze maximum quality from minimum parameters. Scale isn't required for intelligence.
We explore how event-driven SNNs can replace transformer attention for low-power edge inference, achieving competitive performance at a fraction of the energy cost.
RoadmapCustom silicon designed natively for SNN workloads. Here's what we're building, why conventional GPUs are inadequate for brain-inspired AI, and the technical roadmap ahead.
PhilosophyBigger isn't always better. We make the case for small, efficient, private AI — and why sub-billion parameter models are the future of personal computing.
Roadmap
Start of our research program on Spiking Neural Networks — exploring spike-based computation as an alternative to dense transformer attention.
Release of our first proof-of-concept models: Axiom, Lemma and Theorem. Three architectures built on SNN-inspired principles, publicly available for testing.
Start of product development around Lambert — building real-world applications and tooling on top of the model series.
Target: partnerships with research labs that have access to neuromorphic chips. Goal is to run our SNN models on dedicated hardware for the first real benchmarks.
Partnerships with companies building everyday appliances — air fryers, robotic vacuums, and similar edge devices — to deploy ultra-low-power Lambert inference on-device.
Production deployment of Lambert on neuromorphic silicon. No GPU, no cloud — just efficient, always-on intelligence running natively on dedicated hardware.
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