We take regulated, data-rich enterprises from raw proprietary data to a deployed, private foundation model — trained on GPU hardware we own, inside a security boundary we control. Your model. Your weights. Your walls.
Public models don't know your data, your domain language, or how your business actually works — and the gap shows exactly when the stakes get real.
Public LLMs are trained on the open internet, not your domain. They guess at your terminology, your workflows, and your edge cases.
Regulated teams in healthcare, finance, and the public sector can't ship sensitive data to closed third-party APIs — full stop.
A from-scratch model means scarce ML researchers, GPU clusters, and seven-figure budgets before you see a single result.
Leasing someone else's model means no control over the weights, the cost curve, or the roadmap. Prices and terms change under you.
One accountable team owns the entire stack — from your raw data to a model running in production, and keeps improving it.
Private weights, delivered. Your data never leaves your boundary, and you get full control of cost, latency, and roadmap — forever.
Your model trains on our dedicated DGX H100 fleet — owned, not rented from a cloud. Not shared, not exposed, inside a boundary we control.
Reusable model-building technology — data pipelines, training stack, evaluation tooling — that gets better with every model we ship.
Securely ingest, clean, and structure your proprietary data.
Select or design the model architecture for your use case.
Train on dedicated GPUs using our efficiency stack.
Domain evals, safety, and human feedback.
Private deployment, monitoring, continuous improvement.
You can't benchmark a model that hasn't been built yet — you can only judge the people who'll build it. Two proof points: one for research firepower, one for shipping in the hardest regulated environments.
Our team trained an AlphaZero-class engine to International-Master strength in 12 hours on a single consumer GPU — about 600× cheaper than the original run. A chess engine isn't a foundation model, and we won't pretend it is. It's a measure of the team you're hiring: engineers who get frontier-grade results out of modest hardware — the same discipline that keeps your training bill sane.
The other half of the job is production. We deployed a local-first foundation model in behavioral telehealth — on-device inference with ML-derived PHQ-9 depression scoring from audio. Private by default, serving real patients, inside a HIPAA boundary.
We work with data-rich organizations in regulated verticals — where a private, domain-tuned model isn't a luxury, it's the only way to ship AI at all.
Clinical models that keep PHI inside your HIPAA boundary — proven in production.
Sensitive conversations, on-device inference, measurable outcomes.
Auditable models you can stand behind in model-risk review — tuned to your book and your risk language.
Privilege stays intact — documents never leave your boundary. The model comes to the data.
Sovereign, auditable AI on dedicated hardware — not a shared cloud.
We put our own capital into a dedicated NVIDIA DGX H100 fleet so customer models never have to touch rented, multi-tenant infrastructure.
| Giant Leaf AI | Closed LLM API | Hyperscaler AI | AI dev shop | DIY in-house | |
|---|---|---|---|---|---|
| You own the model weights | Yes | No | Partial | Partial | Yes |
| Domain-tuned foundation model | Yes | No | Partial | Partial | Yes |
| Full-stack, incl. dedicated hardware | Yes | No | Partial | No | Partial |
| Proprietary training efficiency | Yes | — | No | No | No |
| Speed to production | Yes | Yes | Partial | Partial | No |
Yes. You receive the trained weights and full rights to run them wherever you choose — your datacenter, your cloud tenancy, or on-device. There is no ongoing dependency on us to keep using your model.
Training runs on GPU systems we own outright, inside a security boundary we control — not a shared public cloud. Your data is used only to build your model, and deployment options include fully on-premise and on-device setups where data never leaves your environment.
Fine-tuning a closed model still leaves someone else holding the weights, the pricing, and the roadmap. We build you a foundation model shaped around your domain from the start — one you own, can audit, and can run at a cost structure you control.
It used to be. Training efficiency is this team's founding obsession — in public research, we reproduced an AlphaZero-class result at roughly 600× lower cost than the original. We bring that same discipline, plus GPU hardware we own outright, to every engagement — so a focused, domain-specific model costs a fraction of the 2020-era playbook.
A typical path starts with a scoped paid pilot to prove value on your data, then moves to a production build. Because one team owns every step — data, architecture, training, evaluation, deployment — there are no hand-off delays between vendors.
Models aren't static. We monitor, evaluate, and retrain as your data and needs evolve — on the same dedicated infrastructure your model was born on. You can run it yourself, or keep us accountable for it in production.
Tell us about your data and your domain. We'll tell you — concretely — what a private foundation model can do for your business, and what it takes to get there.
Talk to our team