LOCAL AI DEPLOYMENTS

Your AI stack.
Your building.
Your hardware.

Frontier open-weight models running inside your firewall. Same interfaces your team already uses. Nothing ever leaves your network.

See What's Leaking →

Built for teams in healthcare, legal, finance, and regulated industries.

THE PROBLEM

Every prompt is a data transfer.

Your contracts. Your source code. Your patient records. Processed on hardware you don't control, in jurisdictions you didn't choose, under terms of service that change without notice.

That's not a workflow. That's a liability.

HOW LOCAL AI WORKS

Same tools. Different architecture.

Open-weight models run on hardware you own. Your team opens a browser. The interface looks exactly like ChatGPT. The difference: every prompt stays inside your building.

Your models

Frontier-class. Open-weight. #1 through #5 on every major benchmark. Downloadable. Deployable. You own the weights.

Your hardware

Runs on a device in your office — not a data center you've never visited. No GPU rack. No server room. Fits on a shelf.

Your data

Every prompt, every document, every query stays inside your network. No telemetry. No training signal. No surprise in next month's TOS update.

THE KILL LIST

Every tool has a local equivalent.

CURRENTLY USINGLOCAL REPLACEMENT

ChatGPT / OpenAI→GLM-5 + Kimi K2.5

Claude / Anthropic→DeepSeek V3.2 + Qwen 3.5

GitHub Copilot→MiniMax M2.5 + Continue.dev

ChatGPT Enterprise→Open WebUI + LiteLLM

Pinecone / Vector DB→Qdrant + Weaviate

VMware / ESXi→Proxmox VE

Same interfaces. Same workflows. Zero data egress.

THE ECONOMICS

API rent vs. ownership.

A 50-person team typically spends $200,000–$400,000 per year on API rent. A local stack pays for itself in 3–4 months.

YEAR 1

Roughly the same spend. Same budget. Completely different architecture.

YEAR 2

They raise prices. You don't. Your cost is electricity. Theirs is another invoice.

YEAR 3+

You've saved mid-six figures. Your competitors have 24 months of their data baked into someone else's frontier model. The gap compounds.

BUILT FOR REGULATED INDUSTRIES

Compliance by architecture — not by paperwork.

When your data never leaves your network, most compliance questions answer themselves.

HIPAA SOC 2 GDPR FedRAMP-ready Attorney–Client Privilege PIPEDA Data Residency

We design deployments for legal, healthcare, financial services, and government teams where data egress isn't an inconvenience — it's a violation.

WHAT WE DO

From audit to deployment. End-to-end.

AI Audit

We map every AI tool your team uses, what data goes in, and where it goes. You get a risk report in 2 minutes — not a sales pitch.

Migration Planning

Which models replace which tools. What the timeline looks like. What your team's day-to-day workflow looks like on the other side.

Deployment Support

Through our partner network, we handle the deployment — from hardware selection to model configuration to team onboarding.

Ongoing Verification

Quarterly zero-egress audits. Model updates as new open-weight releases drop. You never have to think about it.

COMMON QUESTIONS

What CTOs ask first.

Can frontier models really run on-premise?

Yes. The top five open-weight models — GLM-5, Kimi K2.5, MiniMax M2.5, DeepSeek V3.2, Qwen 3.5 — all run on commodity hardware. A 128GB workstation handles a 50-person team. A single rack handles 500.

What happens when a better open-weight model drops?

You download it. No contract renegotiation, no vendor lock-in, no migration project. Part of ongoing verification is swapping models in as stronger releases ship — roughly every 6–8 weeks right now.

Who actually deploys the hardware?

We handle the audit, model selection, and migration planning. Deployment runs through our partner network — vetted operators who handle procurement, install, and day-one support. One point of contact. Clear accountability.

What about our existing Microsoft 365 / Google Workspace tools?

They stay. Local AI replaces the inference layer — ChatGPT, Claude, Copilot, and the vector/RAG pipeline behind them. Your productivity suite, SSO, and identity stack are untouched.

How is this different from Azure OpenAI or AWS Bedrock?

Those are still rent. Your data still leaves your network and sits on infrastructure you don't control, under terms of service Microsoft and Amazon can change. "Private cloud" is not the same as local.

What does a typical engagement look like?

Audit first (free, 2 minutes). Then a 30-minute walkthrough of your risk report. If we're a fit, migration planning takes 1–2 weeks. First tool is usually migrated within 3 days. Full cutover is typically 4–8 weeks depending on scope.

Your AI stack.Your building.Your hardware.