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Furnace lit · ML foundry

Raw data in.Trained models out.

Managed compute for training, serving, data pipelines, and generative rendering — every dataset, run, and artifact owned by the entity that paid for it.

epoch0 / 40
loss2.418
holdout gateheating
01

The line, from charge to cast.

Every artifact walks the same floor — and leaves with its lineage stamped on it.

Ch

Charge

Register a dataset; lineage and licenses are recorded on intake.

dataset v14 · verified
Me

Melt

Train in a Crucible with fixed resources, checkpoints, and preemption recovery.

8×GPU · ckpt / 500 steps
Te

Temper

Holdout and safety evaluations must pass before anything can be promoted.

gate: pass · reviewer signs
Ca

Cast

The approved Artifact goes to serving behind a canary, with per-entity quotas.

canary 5% → 100%
02

What the foundry casts.

The compute plane for teams that would rather ship models than manage clusters.

Plate A · compute

Distributed training

Train at production scale with checkpointing, preemption recovery, and experiment tracking.

Plate B · serving

Model serving

Autoscaled inference with canary rollouts and quotas per entity.

Plate C · data

Data pipelines

Prepare, version, and validate training and live data in the same place.

Plate D · render

Generative rendering

Reproducible 3D and media rendering from a seed and a manifest.

Plate E · gates

Evaluation gates

Holdout and safety checks block promotion until they pass and a person approves.

Plate F · lineage

Lineage

Every artifact traces back to its data, code, parameters, and approver.

03

Every crucible holds its own pour.

Compute is organized like a foundry. The Crucible — one workload — is where collaborators and quotas are granted, so nothing pours into another entity's mould.

Commons

Foundry

The shared compute surface.

Entity

Works

One entity's datasets, budgets, and policies.

Output

Artifact

A finished model, dataset, or render.

04

The smiths.

Forge-gods and fire-tenders — Smelt's personas schedule, test, harden, and promote. Each works under an Idio identity scoped to one Works.

Ilmarinen

Finnish

sampo_smith Forged the Sampo — the machine that grinds out value; the anchor seat

Ogun / Ogou

Yoruba/Vodou

iron_orisha Iron, tools, and the road — hard paths, performance-critical casting

Goibniu

Irish

unfailing_smith The smith whose work never fails — the artifact that keeps working

Kothar-wa-Khasis

Ugaritic

skilled_and_wise_forge 'Skilled-and-Wise' — the precision seat; the god whose name is a spec

Soslan

Caucasian (Nart)

forge_tempered Tempered in the forge — the QA seat; survives production heat

Zarabanda

Palo (Congo)

bond_breaker Breaks all bonds — the adversarial/test seat

Gu

Fon/Dahomey

metal_father 'He of the metal' — the raw-material seat

Hephaestus

Greek

forge_master The smith of Olympus — the classic foundry patron

Tvashtr

Vedic

bolt_forger Forged Indra's thunderbolt — weapon-grade artifacts

Khnum

Egyptian

wheel_shaper Forms beings on the wheel — hyperparameters as clay

Prometheus

Greek

fire_bringer Stole fire — technology transfer to entities that couldn't reach the heat

05

Assay certificate.

The same identity, policy, provenance, and consent controls run under every Colossal Capital product.

ASSAYED
& MARKED
  • AI provenanceEvery AI-assisted result in Smelt is disclosed and traceable — which persona acted, which model ran, on whose authority, from which sources.
  • Built for AI regulationTransparency notices, human oversight, risk documentation, and audit logs designed to support EU AI Act, Colorado AI Act, and NIST AI RMF / ISO/IEC 42001 programs.
  • Private by designEntity-scoped storage, data minimization, encryption in transit and at rest, and GDPR / CCPA-CPRA request handling. Customer data never trains a shared model without opt-in.
  • Fine-grained provisioningIdio SSO over SAML and OIDC, entity context, role and attribute policy, and a named, scoped, revocable identity for every agent.
  • Humans at the gateConsequential actions wait for an eligible approver, and every decision lands in an exportable audit trail.

Private models, at the scope you choose — nothing rolls up on its own.

  • UserOne researcher's training preferences
  • ContentOne dataset or Artifact
  • EntityOne entity's private models
  • OrganizationAn organization's approved corpora

Customer datasets and weights are never pooled; ecosystem learning is a separate, opt-in contribution path. Shared ecosystem learning is a separate, sanitized, opt-in path. AI transparency notice.

Furnace time

Forge models without building the forge.

Smelt is in development. Leave an email for early cluster access.