90%
Llama 8B calls avoided at 10x recurrence
Do not just prompt the model. Teach the system.
A local, teachable memory layer that sits around your models. It preserves verified work, corrections, context, and route state so expensive model work runs only when needed, on a single edge device or a multi-GPU server. Today it is a family: Studio for reuse, a drop-in Gateway for any provider, Embodied for robots, and Fleet for many devices.
From Jetson at the edge to x86_64 multi-GPU servers in the data center. 7-day free trial per device.
NeuraFrame started as a reuse engine. The same teachable memory now shows up in four ways, so you can put it wherever your model work happens.
The reuse engine. It preserves verified work and corrections so repeated model work is not recomputed, on your own hardware. Studio docs
The drop-in. Put it in front of any model provider with a one-line change and serve repeats from memory, with no code change. Gateway docs
For robots. Train in simulation, ship the memory to the machine, and it keeps learning. No model on the robot. Why Embodied
For many devices. Curated round-trip learning: devices learn, you approve what should spread, and it is distributed back, signed. How Fleet works
Modern AI systems repeatedly process the same prompts, documents, images, workflows, corrections, and context. That repeated work costs latency, energy, GPU time, tokens, and money.
NeuraFrame Studio™ sits around existing models and local workflows. It tracks verified results, corrections, context, and route state so familiar work can be reused, uncertain work can be reviewed, and novel work can still go to the model.
For Jetson Orin, ARM64 edge devices, robotics, and local AI systems.
For Linux workstations, servers, local LLM hosts, and development machines.
If the license expires, NeuraFrame™ enters pass-through mode. Your model can still answer directly.
90% heavy GPU calls avoided at 10x recurrence.
10.03x recurrence speedup with 100% agreement.
87.1% prompt-token reduction with 100% factual accuracy.
Unchanged rerun required zero LLM calls.
Raise it in simulation, ship the memory to the robot, and it keeps learning. Train a NeuraFrame™ instance on a server with your heavy model, then deploy only NeuraFrame™ to the machine. No model and no GPU on the robot: it carries just the learned memory and a light engine, acts on what it has learned in milliseconds instead of running a model for seconds, and escalates what it has not learned instead of guessing.
The heavy model trains it in simulation, where compute is cheap. The robot runs only the memory, so it needs no on-board GPU and far less power.
Export the learned experience to one portable file and copy it to the machine. Milliseconds from memory, not seconds from a model.
It learns on-device from corrections and escalates rather than guessing. The safety rules are a tool, not a guarantee.
Download NeuraFrame Studio™ for ARM64 or x86_64 Linux and start a 7-day free trial per device.