frontier reasoning, distilled into something you keep.
Don't rent intelligence. Mint it.
A 400B+ frontier teacher can reason brilliantly about your domain — but it lives in someone else's cloud, metered per token, and gone the day it's deprecated. Nucleus does the opposite: it extracts that reasoning and crystallizes it into a 1–7B model you own, running on the hardware in front of you.
This is not RAG and it is not prompt engineering. The Super Skill doesn't look things up at inference time — the knowledge is trained into its weights. It knows. The metric we optimize is Wisdom per Watt: permanent, deployable reasoning sharpened per compute cycle invested.
A 400B+ teacher's reasoning is pulled through an adversarial swarm that interrogates, challenges, and corrects a small student until it can't be broken. What comes out the other side is a 1–7B model that reasons like its teacher inside your domain — and carries a cryptographic seal proving exactly how it was made.
KICE and TICE agents extract 7 layers of knowledge — from edge cases to unwritten expert folklore.
Symbolic Chain-of-Thought + RAFT teach structured reasoning, not surface token patterns.
The swarm hunts every failure mode and feeds it back, retraining until convergence.
An Ed25519 provenance chain mints the model's DNA — verifiable, and instantly revocable.
Graduation isn't a test it passes. It's the moment the swarm gives up trying to break it — and the seal goes on.
Every step is event-driven over NATS, traced in Jaeger, and metered in Grafana. If it isn't observable, it doesn't exist — and nothing ships without a provenance chain.
Point it at your raw folder — docs, scans, mailing lists. It scans, OCRs, tags, and chunks. No cleaning or structuring required from you.
Parallel agents mine certified knowledge and tacit expert know-how — rare concepts, edge cases, gotchas, folklore — before training begins.
Reasoning is decomposed into explicit symbolic steps; oracle docs plus deliberate distractors train the student to reason through noise.
Interrogate → challenge → evaluate → correct → synthesize → retrain, in a loop with no fixed cycle limit. AutoResearch evolves the rubrics that drive it.
General-capability regression, domain-mastery verification, and a hallucination & faithfulness audit. All three pass, or no seal.
Teacher SHA-256 + corpus hash + pipeline config + audit, signed Ed25519. Cryptographic proof of how the model was made — monitorable and revocable.
Rent the math, or own the mind.
Inside the domain it was forged for, a 3B Super Skill can match or beat a 500B teacher — at a fraction of the size, with near-zero marginal cost per query and no data ever leaving the machine.
And Phase 7 isn't a terminus. Your v1.0 ships immutably — yours, forever — while the pipeline keeps cycling toward v1.1 against an evolving corpus. It never pushes; you pull when, and if, you choose.
A 3-billion-parameter model that out-reasons a 500B teacher inside its domain — air-gapped, owned, and only sharpening over time.
Who else ships a model that beats its teacher and answers to no one but you?
Hand Nucleus a teacher and a corpus, and walk away with a sealed Super Skill that reasons like a frontier model — on hardware you already own.