We build neural networks that are radically smaller and faster than conventional models — without sacrificing accuracy. Proprietary quantisation technology that delivers production-ready AI for environments where size, power, and latency matter.
Each BitBrain model is trained from scratch using our proprietary pipeline. Two weight systems — PytBit (ternary) and HypBit (quinary) — tuned to the task. We train the model. You license and deploy it.
BitBrain state-space architectures for real-time audio analysis and manipulation. Capable of complex tasks like speech noise suppression, acoustic echo cancellation, and keyword spotting. Operates with zero multiplications, enabling continuous deployment on ultra-low power devices.
Lightweight convolutional and state-space architectures for visual inference. Capable of handling tasks like object detection, facial recognition, and automated quality control. Maintains high accuracy without float32 multipliers, ideal for continuous camera processing.
Efficient sequential models for language modeling, anomaly detection, and predictive analytics. The state-space architecture enables constant-cost continuous inference — achieving deep contextual understanding with zero attention overhead and no memory scaling.
Our training pipeline is model-agnostic. If a neural network can solve your problem, we can build a BitBrain version of it — dramatically smaller, deployable on your target hardware.
Extreme quantisation should destroy model accuracy. Ours doesn't. A proprietary training pipeline makes the difference.
BitBrain models aren't compressed after training — they're trained quantised from the first step. PytBit uses ternary weights {-1, 0, +1}. HypBit uses quinary {-2, -1, 0, +1, +2}. Every forward pass uses integer additions only. No floating-point multiplications anywhere in inference.
Quantisation introduces systematic errors that compound through layers. We developed a proprietary correction mechanism that operates during training — continuously detecting and compensating for quantisation noise without adding inference cost. The result: accuracy that standard quantisation can't match.
Instead of attention (which scales quadratically and requires per-user KV caches), BitBrain uses state-space models with constant-time, constant-memory inference. A single model instance serves unlimited concurrent users. Conversation state is kilobytes, not gigabytes.
Models export to a single portable file. Inference is pure integer arithmetic — runs in any browser, on any microcontroller, in any language. No framework dependencies. No GPU. No runtime. The same model runs on a cloud server and a hearing aid.
Train a large float32 model, then compress it afterwards. Quality degrades. The model wasn't designed for the constraints.
Train directly in the target precision from step one. The model learns to be accurate within its constraints.
Our technology isn't just about smaller models today. The same properties that enable extreme quantisation unlock entirely new deployment paradigms.
BitBrain models running on standard hardware today. Browsers, microcontrollers, edge devices, and plugins. Proven on audio, vision, and sequence tasks.
Stateless SSM architecture enables a single model instance to serve unlimited concurrent users. No per-user memory. Server costs approach zero per session. Exploring how the architecture scales with compute.
A multiply-free, noise-resilient architecture maps directly onto analog circuits. Every component has an analog primitive counterpart. No ADC/DAC conversion between layers. Physics does the compute. SPICE-validated at scale.
Whether you need a model for a specific problem, want to license an existing BitBrain model, or are exploring what efficient AI could do for your product — we'd like to hear from you.