Private AI That Costs $500K Elsewhere.
From $2.5K/mo.

Enterprise vendors charge half a million a year for private LLM deployments. We run local LLMs on our own GPU cluster — SOC2 certified, air-gap ready, zero third-party API exposure. Your data never leaves your environment.

Owned GPU infrastructure. Not rented cloud instances — our own hardware, meaning we can price this at a fraction of what IBM, Palantir, or Booz Allen charge.

What Private AI Actually Costs

Every enterprise AI vendor quotes six or seven figures for private LLM infrastructure. We own the GPUs, so we don't.

Palantir AIP
$1M+/yr
Multi-year contracts. 6-month deployment.
IBM watsonx
$500K+/yr
Enterprise licensing + consulting fees.
Booz Allen
$750K+/yr
Government contracts. Long procurement.
Secure AI
$30K/yr
No contract. Owned GPUs. Start in weeks.
Save $470K+ vs. IBM

What Happens When You Use Their APIs

Every query to OpenAI, Anthropic, or Google trains their models. For security intelligence, that means your threat assessments, principal movements, and operational plans are someone else's training data.

Exposure risk
Executive Travel Routes Leaked

A Fortune 500 company used ChatGPT to draft travel risk assessments. Prompt data — including executive names, hotel locations, and convoy routes — was ingested into OpenAI's training pipeline. With Centinela, that data never leaves your GPU cluster.

Scenario: Composite from reported enterprise AI incidents
Compliance failure
ITAR Violation via Cloud AI

Defense contractors processing threat intelligence through commercial APIs risk ITAR violations when controlled technical data crosses jurisdictional boundaries. Our air-gapped deployments eliminate this vector entirely.

Scenario: Based on published ITAR compliance guidance
Data sovereignty
UHNW Principal Pattern Exposure

Family offices using cloud AI for threat monitoring inadvertently create a pattern-of-life database on their principals — travel patterns, threat responses, security protocols — all stored on someone else's servers.

Scenario: Common pattern in UHNW security consulting

Your Private Intelligence Stack

Not a managed service on someone else's cloud. Dedicated hardware, your models, your data.

secure-ai-status — centinela-infrastructure
ALL SYSTEMS NOMINAL
MODEL .......................... Local LLM (Private)
GPU CLUSTER .................... Owned Infrastructure
OSINT Collection Engine ........ ACTIVE
NLP Analysis Pipeline ........... ACTIVE
Compliance ..................... SOC2 Certified
Third-Party API Exposure ........ NONE
Client Data Leakage ............. ZERO
Air-Gap Option .................. AVAILABLE
Custom Model Fine-Tuning ........ SUPPORTED
Owned
GPU Infrastructure
Private
Local LLM Deployment
0
Third-Party API Calls

Built For Organizations That Can't Compromise

When your intelligence operations touch classified programs, UHNW principals, or regulated data — you need infrastructure that never leaks.

Defense & Government Contractors

ITAR-compliant intelligence processing. No data touches commercial AI APIs. Full audit trail for compliance.

Family Offices & UHNW Principals

Travel intelligence and threat monitoring that never exposes principal identity or movement patterns to third parties.

Regulated Industries

Financial services, healthcare, and energy companies with strict data handling requirements and board-level oversight.

Corporate Security Teams

Organizations that need AI-powered intelligence but can't risk operational details flowing through public model APIs.

Stop Paying Enterprise Prices for Rented Infrastructure

Palantir and IBM charge $500K+ because they rent cloud GPUs and mark them up 10x. We own our hardware. That's why we can offer Secure AI at a fraction of the cost.