Private AI Infrastructure & Governance
Maintain full control over your data and models. Arc Analytics helps organizations design and deploy secure, in-network AI environments that prioritize privacy, ownership, and restricted access.
Enterprise AI Without the Privacy Risk
Large-scale AI adoption often stalls because of concerns over data leakage and vendor dependence. We help organizations move past these barriers by building infrastructure where your proprietary data stays behind your network, and your models run in environments you control.
Secure Infrastructure & Controls
Core Capabilities
Private Deployment
Deploying models within your private cloud or on-premises environment to ensure zero data retention by third parties.
Governed Access
Defining who can use specific tools and what data those tools can access through strict permission layers.
Secure Data Retrieval
Building retrieval systems (RAG) that allow AI to use your internal documents without exposing them to public training sets.
Resource Optimization
Selecting the right hardware and environment to balance performance, latency, and cost-efficiency.
Connect AI to Your Real Operations
Stop experimenting with disconnected tools. Talk with Arc Analytics about building a governed, integrated AI workflow that safely streamlines your daily operations.
Why Private Infrastructure Matters
Feature
Data Ownership
Model Access
Security Perimeter
Data Retention
Recurring API Costs
Public AI Service
Shared with Vendor
Standard / Restricted
Public Cloud
Vendor Policy
High / Unpredictable
Private Infastructure
100% Internal
Full Customization
Your Private Network
Zero External Retention
Managed & Predictable
Our Governance Layer
1
Risk Matrix
Assessing and scoring the risk level of every AI-enabled tool
2
Permissions Audit
Regular reviews of which users and systems are connected.
3
Human-in-the-Loop
Designing “Final Approval” steps for all AI-generated outputs.
4
Audit Logging
Tracking every interaction and data call for compliance.
5
Policy Framework
Establishing clear rules for internal AI usage.
The Rise of In-Network AI
As API costs rise and model performance improves, many organizations are moving toward local AI hosting. Running open-source models behind your own firewall is no longer a luxury; it is a strategic requirement for organizations in the public sector, healthcare, and education.
We help you navigate the hardware requirements, software orchestration, and network configurations needed to run powerful models locally.
We help you navigate the hardware requirements, software orchestration, and network configurations needed to run powerful models locally.
Experience in Regulated Environments
SLED Specialists
We understand the security demands of State and Local Government and Education
Hands-On Implementation
We don’t just deliver a plan; we stand up the environment.
Privacy First
We prioritize data protection at every layer of the hardware and software stack.
Frequently Asked Questions
Can we run AI on our own hardware?
Yes. We help organizations evaluate their existing hardware or specify new infrastructure to run open-source models locally or in a private cloud.
What is "Local AI"?
? Local AI refers to running Large Language Models (LLMs) on servers that you own or control, rather than sending your data to an external provider via an API.
Is private AI more expensive?
While there is an upfront setup cost, private infrastructure often reduces long-term operational costs by eliminating unpredictable API credits and monthly per-user fees.
How do you handle hallucinations?
We use Retrieval-Augmented Generation (RAG) and human-in-the-loop workflows to ensure the AI’s answers are grounded in your specific, trusted internal data.
How long does it take to stand up a private environment?
For many organizations, we can stand up an initial in-network environment and connect basic systems in less than a week.