Now accepting enterprise engagements

Your AI.
Your Building.
Your Data.

This stopped being about where your AI runs. It’s about whether your company owns its intelligence — or rents it from someone who can change the terms tomorrow.

We deploy production-grade AI on hardware you own. No cloud dependency. No data leakage. No vendor lock-in.

“Where were you when you found out your competitor was using the same AI — trained on the same data — including yours?”
“Where were you when you found out you spend $15 per million tokens — when you could run the same model for 50 cents?”
“Where were you when you found out ‘on-prem data’ and ‘on-prem AI’ are not the same thing?”
“Where were you when you found out your company has 147 AI tools — and IT only knows about 12 of them?”
“Where were you when you found out you could have on-prem AI running in the time it took to read the first consulting report?”
Schedule a Discovery Call → See Our Services
✓ Open-Source Models
✓ On-Prem Deployment
✓ Enterprise Security
✓ Dedicated Engineer
The Questions Nobody’s Asking
Question 01 / 05
Where were you when you found out every question your team asks ChatGPT becomes training data for someone else’s model?
Question 02 / 05
Where were you when you found out your entire AI infrastructure runs in someone else’s building — and they can change the terms tomorrow?
Question 03 / 05
Where were you when you found out the consulting firm charging you $1.2M just recommended you buy another company’s cloud AI?
Question 04 / 05
Where were you when you found out your AI vendor’s audit trail is stored in their own database — and they can edit it?
Question 05 / 05
Where were you when you found out you don’t have to accept any of this?
You already know what you need to do. Let’s talk. →
The Problem
That feeling your CFO has about AI costs? That’s not resistance — that’s fiduciary instinct.
Data centers now consume 565 terawatt-hours of electricity globally. AI servers account for 31% of that demand — and it jumps 26% every year. Every token your company sends to the cloud powers that machine, carries your proprietary data, and adds to the bill.
$15
Per million tokens (cloud)
→
$0.50
Per million tokens (on-prem)
565
Terawatt-Hours / Year
Global data center electricity consumption in 2026. Up 26% year-over-year. AI is the fastest-growing demand source.
2025
4.1%
2026
5.3%
2027 (proj)
8.5%
US peak summer power demand consumed by data centers
What We Deploy
Three architectures. Proven tools. Your hardware.
Every engagement follows one of three standard architectures. Configuration varies by industry. The architecture does not. Repeatability means faster deployment and lower risk.
💬

Private Chat

⏲ 4–6 weeks to production

Replace ChatGPT and Claude with a model running behind your firewall. No RAG, no fine-tuning, no agents. Just fast, private inference for your team.

  • On-prem LLM (Llama, Mistral, Phi-4)
  • Web-based AI workspace
  • SSO integration with your directory
  • Monitoring and uptime SLA
📚

Private RAG

⏲ 8–12 weeks to production

Chat plus knowledge retrieval against your own documents. Your team asks questions and gets answers grounded in your data — without any of it leaving the building.

  • Everything in Private Chat
  • Vector database for your documents
  • Document ingestion pipeline
  • Role-based access controls
  • Workflow automation integration
⚙

Private SLM

⏲ 16–24 weeks to production

A model fine-tuned on your company’s data, with autonomous agents handling real workflows. Full integration with your business processes.

  • Everything in Private RAG
  • Custom model fine-tuned on your data
  • Autonomous agents for key workflows
  • Enterprise security hardening
  • Dedicated on-site engineer
  • Compliance documentation
Discuss Your Requirements →
How We Work
Six phases. No surprises.
We follow the same delivery sequence for every engagement. You know what happens, when it happens, and what you get at each milestone.
1

AI Audit

Inventory every AI tool, data flow, and compliance gap in your organization.

2–3 weeks
2

Infrastructure

Rack, network, firewall, monitoring, VPN. The foundation before any AI workloads.

2–4 weeks
3

First Inference

Model running. Workspace deployed. Your team uses on-prem AI for the first time.

2–4 weeks
4

Fine-Tuning

Your data collected and cleaned. Model trained on your domain. Benchmarked against base.

4–8 weeks
5

RAG + Agents

Document pipeline live. Autonomous agents for your top use cases. Real workflow integration.

3–5 weeks
6

Harden + Handoff

Security hardened. IT staff trained. Managed services activated. We stay.

2–3 weeks
Why AI Standards
We show up. We build. We stay.
There’s a kind of company that’s been through three consulting engagements, two cloud migrations, and a compliance audit — and the AI still doesn’t work the way it was supposed to. Nobody ever asks them if the current path is sustainable, because they’re so good at making it look fine.
Dimension Traditional Consultants Cloud AI Vendors AI Standards Inc
Approach Audit, report, leave Sell you API access Audit, build, stay
Where AI runs Their cloud recommendation Their data centers Your building
Your data Sent to their cloud Trains their next model Never leaves your premises
Time to production 6–18 months Weeks (cloud), no on-prem 4–12 weeks (on-prem)
Ongoing presence Quarterly check-in Support ticket queue Dedicated on-prem engineer
Vendor lock-in Recommends proprietary stack Locked to their platform 100% open-source. Walk away with everything.
If they shut down Your report is still a PDF Your AI goes dark Your system runs independently
The Stack
Proven tools. No vendor lock-in.
Every component we deploy is open-source and battle-tested. You own everything. If we walked away tomorrow, your AI keeps running.
⚙

Inference

Ollama · vLLM · llama.cpp

🤖

Models

Llama · Mistral · Phi-4 · DeepSeek

🔍

Knowledge

Qdrant · Docling · nomic-embed

🛠

Orchestration

n8n · LangGraph

🔒

Identity

Keycloak SSO · AD/SAML

🛡

Security

HashiCorp Vault · Wazuh SIEM

📈

Monitoring

Prometheus · Grafana · Netdata

📡

Network

OPNsense · Tailscale · TLS

Industries
Configured for your regulatory environment.
Same architecture. Different compliance requirements. We know the difference between HIPAA logging and SR 11-7 audit trails.
🏦

Financial Services

SR 11-7 · SOC 2 · Model risk management

🏥

Healthcare

HIPAA · PHI protection · BAA documentation

⚖

Legal

Matter-based access · Privilege controls · eDiscovery ready

🌍

Defense & Government

Air-gapped · CMMC 2.0 · Zero-trust

⚒

Manufacturing

Technical documentation · CAD ingestion · Quality systems

💻

Technology

Code repositories · CI/CD integration · Developer tools

Leadership
Senior people. Real experience.
No junior analysts running your deployment. The people who designed the system are the people who build yours.
JA

Joe Armbruster

Chief Executive Officer

20+ years in cybersecurity and enterprise technology. CISO-level experience across financial services and defense.

FA

Fran Alvarez

Chief Technology Officer

Systems architecture and AI infrastructure. Designs the deployment framework and oversees all technical operations.

AK

Anwesh Kumar

Lead Engineer

Full-stack engineering and system architecture. Builds and deploys the infrastructure your team uses every day.

Get Started

You’re allowed to want AI that belongs to your company.

A year from now, when your AI runs on your hardware, your data never leaves, and your costs dropped 80% — you’ll look back at this conversation as the moment it started. Book a 45-minute discovery call.

Schedule a Discovery Call →
🔒 We sign a mutual NDA before the conversation starts. Your information is protected.

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