Models

Models you own.

Open-weight domain models from the Neural Infrastructure family — published on Hugging Face, proven on our own live deployments, and yours to run anywhere: our fleet, your cloud, or your own hardware.

Why owned models

Rented intelligence is a dependency. Owned intelligence is an asset.

API models are someone else’s weights, someone else’s terms, someone else’s price changes. An owned domain model is a file you hold — auditable, portable, and running wherever your data lives. It’s layer 05 of the stack, made literal.

Weights you hold

Apache-2.0 licensed adapters you can download, inspect, and serve on any infrastructure.

Provenance you can audit

Every release ships with its evaluation results, and every training run is leak-tested so no client or brand data rides along.

Run at your altitude

Serve them on our managed fleet, inside your VPC on the Own tier, or on your own keys and hardware.

The family

Domain brains, not general chatbots.

Each model is trained for one job an AI employee actually does — and evaluated blind against its own base model before it ships.

Flagship
Marketing

B2B Marketing v2

The copywriting brain behind our marketing AI employees — positioning briefs in, on-brand B2B assets out.

  • LoRA adapter on Qwen3-32B · Apache-2.0
  • Blind-judged 7.16 vs 4.79 for its base model
  • Preferred head-to-head over base in 86% of matchups
  • Fabricates fewer statistics than its own base
  • Zero-leak verified — no client or brand data inside
Open weights · Hugging Face
View on Hugging Face
Marketing

B2B Marketing (7B)

The compact first release — the same B2B copy discipline, light enough for a single modest GPU.

  • LoRA adapter on Qwen2.5-7B-Instruct · Apache-2.0
  • 13,000+ curated, brand-scrubbed training pairs
  • Zero-leak verified across generation tests
  • Suited to on-device and single-GPU serving
Open weights · Hugging Face
View on Hugging Face
New
Finance

CFO Brain v1

The advisory brain behind our Sr. Finance Analyst AI employee — your agent brings the live numbers, this model brings the judgment.

  • LoRA adapter on Qwen3-32B · Apache-2.0
  • Preferred head-to-head over base in 75% of matchups
  • Grounded arithmetic — every derived figure shows its formula
  • Won’t state live rates as fact: 8/10 calibration vs base’s 5/10
  • Zero-leak verified — no client or brand data inside
Open weights · Hugging Face
View on Hugging Face
Proof, not promises

This model wrote this website.

The marketing model’s acceptance test was real work: drafting new landing copy for this site from our positioning brief. The drafts were judged a significant improvement on the copy they replaced, refined by a human editor, and shipped — the hero you read on the way in came out of that session. Read the full build log →

7.16 vs 4.79
Blind judge score vs base
86%
Head-to-head preference vs base
0
Brand or client data leaks
Apache-2.0
License on published adapters
How they’re made

The dataset is the moat. The method is the guarantee.

Base models are a commodity — the value is in the training data and the discipline around it.

Curated pairs, not scraped sludge

Training data is generated against real business scenarios, then quality-filtered by calibrated judges — the weak majority gets cut before training starts. The CFO brain kept just 17% of what was generated.

Blind evaluation before release

Every model is scored blind against its own base across held-out prompts. If it doesn’t beat the base, it doesn’t ship. Read a full build log →

Anti-fabrication testing

Outputs are audited for invented statistics and unsupported claims — and for finance, every derived figure is recomputed by a dedicated math verifier. A failure mode we test for explicitly, not hope away.

Scrubbed and leak-tested, every run

Client and brand data is scrubbed before training and verified absent after it. Public weights carry zero customer material.

Put an owned model to work.

We’ll map which of your workflows deserve an owned domain model — and how to serve it at your altitude. No pitch deck. Just a technical conversation.