An efficient model running locally on standard hardware — private by design, built to do more with less. No data center required.
Efficiency is the throughline. The model and the operating system are one system — built together, not one dropped into the other later. If the pieces fit together, they belong in one system, not five separate ones. We build now, and show what we have built. Start with the router. Prove the architecture. Scale to the data center.
A purpose-built memory substrate, verified piece by piece, so the model runs efficiently on ordinary hardware.
Designed from scratch for the edge — small to start, proven before it grows.
The model and the operating system are one system, not a model dropped into an OS later.
For fifty years, chips got faster the same way: smaller transistors, more power. That worked until it didn't. Now physics has hit a wall. Transistors are so small they leak. Crank the power and you just get heat, not speed.
The industry's answer is to build bigger datacenters and burn more power. That's not a solution. That's just spending more to get the same thing. The real answer is the opposite: use less. A model that does the same work on low compute cycles. An operating system that doesn't waste them. A CPU that doesn't have to run as hard — so it doesn't run as hot, and it doesn't break as much. That's efficiency. That's the whole company.
Legacy AI keeps memory in a volatile context window — the whole conversation re-processed every turn, in power-hungry RAM, gone when the session ends. Radiate keeps memory as an append-only record on your own hardware — what happened stays in order, and what matters is sealed and kept. The system remembers what matters — what happened, what was said, what you did. Your memory is yours — it never becomes part of the model. Private by design. Deletable for real. Yours to keep or throw away.
Memory lives on the user's own hardware — and never leaves it. There is no server copy, because there never was one.
Daytime collects lightweight observation logs; nighttime consolidates them into sealed memory tokens, offline. Some memories fade with time unless they're recalled. The important ones are kept.
The bytes are gone, and there is no server copy, because there never was one. The keys never leave the device. We get no copy.
An operating system built around the memory system — we call it Neural OS. Everything the system knows lives in memory, not in files — found by content, not by file paths. Memory is built into the kernel's execution path, not layered on top of it. When we tested the operating system, reaching memory cost almost nothing — recalling what it knows takes almost no compute, and it feels instant, because memory is the system, not something bolted on. The operating system is the product as much as the model is — they ship together, as one system.
A model should not learn live while it is running. Live learning drifts, reinforces wrong associations, and can't be audited. Learning happens between passes — a nightly consolidation — while the system is at rest. The shipped model is frozen and deterministic: what you test is what ships. A bigger model mentors the smaller one — associations first, then quizzing and encouraging questions. Memory itself is a learned capability — the model has to learn what to store, when to check it, and how to use it. Its own training stage, after the basics are solid, before fine-tuning.
The running model does not change itself. No drift, no wrong associations reinforced in production, nothing that can't be audited.
Learning happens between passes, while the system is at rest — the day's observations consolidated into sealed memory, offline.
What you test is what ships. The model that passed its checks is the model that runs.
The cloud brute-forces it: megawatt datacenters re-processing the same context, turn after turn. We cut the redundant work: the system doesn't re-parse its whole history every turn — what it knows, it keeps. Efficient models that don't need a datacenter — running on our own hardware instead. Next: a hosted efficient-model API, run on our own hardware — an optional service, not a dependency; the model itself runs locally, no cloud required. Later: the full model and operating system as one system, scaling to a single rack — Data Center in a Shelf. The model and the OS are designed as one system from the start — the phases are about what ships first.
Your data stays on your hardware. The system does more with less — low compute cycles, no wasted work.
We don't publish a number we haven't measured. Benchmarks and power claims wait until they survive rigorous, reproducible testing. Nothing assumed, nothing fabricated.