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Controlled autonomous AI · offline & multi-persona

A team of specialist AIs, commanded on a single laptop.

Neuron AI is a controlled, autonomous multi-troop AI for commodity Linux hardware — no GPU, no cloud. A commander deploys the right specialist model for each mission across 259 knowledge domains, grounds answers in your own knowledge, remembers across sessions, and improves with every task — all under governance you control.

Multi-domain by design

One commander. 259 fields of expertise.

259knowledge domains
48specialist clusters
47specialist troops
0cloud dependencies
What Neuron AI can do

A commander, a memory, and a team of troops.

Built end-to-end on a Sacred Layer of identity and rules above a working runtime.

Commands a team of troops

A CEO-style commander auto-selects and deploys the best-fit specialist “troop” — each its own local model — for the mission. A second, always-warm reasoner gives an independent opinion.

Remembers with a neural graph

A neural memory and knowledge graph — remember, recall, link and explore — plus cross-session memory and decision logs. Context that compounds, entirely on-device.

Learns from every mission

A lessons-and-feedback loop captures what worked and feeds it back; per-troop after-action reviews make the next run better than the last.

Compresses its own models

TurboQuant compression runs capable models inside a laptop’s memory — benchmarked, estimated and validated — so commodity hardware punches above its weight.

Governs — and defends — itself

Red-team challenges attack it on a schedule, while claim- and grounding-guards, approval gates, an audit trail and policies sit under a Sacred Layer of fixed identity and golden rules — it hardens itself before an adversary can, autonomy with a leash.

Operates your machine

Git, Postgres, SQLite, the filesystem, the shell, Kubernetes and HTTP — Neuron AI does real dev and ops work, not just conversation. It profiles the hardware and picks the right model for the job.

Retrieval-grounded answers

Retrieval-augmented generation over an offline corpus, with each persona paired to its own scoped knowledge — specialists that actually know their field, anchored to your documents.

Briefs you, tracks the work

A daily brief and an operator cockpit; project goals, progress and next actions are tracked so the system stays accountable to the mission.

Masters 259 knowledge domains

Forty-eight specialist clusters span 259 knowledge domains — from cybersecurity and law to medicine, finance and the humanities. Each troop is trained and graded on the domains it owns, and the system tracks its own confidence, domain by domain.

Reflects, heals and evolves

Beyond learning: a metacognition layer that watches its own blind spots, a self-healing loop that detects and repairs drift, and LoRA and “DNA” pipelines that let troops evolve new skills — a system that grows itself, under your governance.

Checks its own beliefs

A belief layer over its stored facts notices when two of them contradict and resolves them by confidence — the higher-weight claim wins, the loser is superseded, never silently dropped. It reasons over what it believes, not only what it stored.

The engineering story

Optimising the half of latency everyone misses.

On CPU-only hardware, a “fast” query took ~90 seconds. The obvious suspect is token generation — and it’s the wrong place to look.

1
DiagnoseProfiling showed the latency was dominated by prompt prefill — the model reading the system prompt and context before writing a single token. On CPU you feel every token of it; generation wasn’t the bottleneck, the prompt was.
2
Re-architectAn effort-routing layer keeps the prompt path lean for common queries and only escalates context and model size when the question genuinely warrants it.
3
ResultCommon-query latency dropped roughly four-fold — from ~90s to ~22s — on the same 14 GB CPU laptop. No new hardware; compute simply spent where it actually goes.
One real number, honestly stated. The ~4× win is measured on a 14 GB CPU-only laptop for common queries — not a synthetic benchmark, and not extrapolated to hardware we don’t run. Neuron AI is in active development; the commander, memory and ops layers are working today.