ReClaim · MOSES

Train a stronger model on a fraction of the footprint.

A proprietary core lets you build and run more capable models on far less — on your own hardware, inside your own perimeter. You get the outcome. The method stays ours.

Request access → proprietary core · sealed
On-device
Trains on one desktop-class GB10
~16 GB
To ship a model — not 100 GB+
8B-class
Real models, built end to end
Provable
Every ship decision, auditable
Your keys
Your models, your silicon
What you get

The advantage is yours to use. The recipe isn't yours to worry about.

You don't buy our method — you buy its result: stronger models that cost less to build, move, and run, without handing your data or your roadmap to anyone.

Do more with less

A more capable model at a dramatically smaller footprint — cheaper to train, lighter to serve, easier to move between environments.

Own it end to end

Your corpus, your weights, your infrastructure. Nothing leaves your perimeter, and no vendor sits between you and your own model.

Ship with receipts

Every release is a reproducible, auditable decision — so you can prove exactly why a model shipped, to a reviewer or a regulator.

The platform

A complete, governed path from your data to a shippable model.

Around the proprietary core sits everything a team needs to build a model they actually own — instrumented and gated at every step.

01 · PREPARE
Curate
Validate & stage your corpus
02 · TRAIN
Build
On your hardware, your control
03 · EVALUATE
Measure
Dozens of suites, continuous
04 · GATE
Decide
Deterministic release checks
05 · SHIP
Deliver
Lean, portable, provable

Trains on your hardware

The same build runs on a single desktop-class NVIDIA GB10 with 121 GB of unified memory, or scales to an 8×GPU host. No datacenter dependency — nothing leaves your perimeter.

DGX Spark · GB108×GPUone build, both

Your keys, any model

The operating agent runs on your own provider keys through one layer — OpenAI, Anthropic, Google, Bedrock, Azure, or local servers. Keys stored server-side, masked, never echoed.

bring your own keyslocal or cloud

Measured, not estimated

Dozens of standardized evaluations plus custom suites run continuously, so quality and regressions surface before anything is considered for release.

32+ eval taskscontinuous

Lean, movable checkpoints

Export a runnable model at a fraction of the usual size — enough to run inference or seed the next run, without a hundred gigabytes of training state.

~16 GB exportvs 100 GB+
Control & governance

Nothing ships on a hunch.

Release is a computed decision, not a judgment call — the same way, every time, on infrastructure you control.

Deterministic gates

A model is cleared for release only when it clears defined thresholds. The check is code, not opinion — so the record is the proof.

Reproducible by design

The same inputs produce the same decision on every run. No drift, no "you had to be there" — a result you can re-derive.

Authorized, single-use

Every consequential action is scoped, time-boxed, and one-time — a clean audit trail from decision to deployment.

Why us

The core is sealed. That's the point.

Most vendors will tell you exactly how they work — which means your competitors can read it too. We don't. Your advantage is durable precisely because the method behind it isn't on a webpage, in a paper, or in a pitch deck.

Qualified teams see the whole picture — the numbers, the architecture, a live demonstration — under NDA. Everyone else sees the results.

SEALED · NDA
— proprietary core —
Early access · ReClaim

Get the advantage. Keep it to yourself.

We're working with a small group of teams that build and ship their own models. Tell us what you're training and where — we'll take it from there.

Proprietary technology. Figures describe operational characteristics of the platform; performance details are shared with qualified teams under NDA.