// PRIVATE AI & LOCAL LLM
Your data stays on your servers. No exceptions.
Cloud AI is useful — until you're pasting in patient charts, attorney work product, or unreleased financials. Private AI runs on hardware you own, inside your network. Queries don't leave. Data doesn't train anyone else's model. You stay in control.
How it works
Everything runs inside your network.
The model, your documents, and every query stay behind your firewall on a VLAN-segmented inference server. Nothing phones home.
Your team
Browser or desktop app
LAN only
Inference server
On-prem hardware, VLAN-isolated
local read
Your documents
Internal files, databases, policies
blocked
Internet / cloud
Data never reaches here
Who it's for
Any organization where data can't leave the building.
If your risk is regulatory, contractual, or competitive — local inference keeps the blast radius at zero.
Healthcare & Billing
Patient charts, clinical notes, and billing data are processed locally. No PHI leaves your network. HIPAA-aligned access controls and audit hooks built in.
Legal & Finance
Attorney work product stays privileged. Financial records stay behind your perimeter. Queries against your document library never touch a cloud endpoint.
HR & Professional Services
Personnel files, investigations, and compensation data don't train anyone's model. Local inference keeps sensitive people-data exactly where it belongs.
R&D & Product Teams
Unreleased roadmaps, trade secrets, and proprietary research stay competitive. No vendor ingests your IP in exchange for a chatbot.
What a deployment looks like
From scoping call to live inference — typically in two weeks.
Scoping call
We map your workload — what you want the model to do, what data it needs to access, and what your network looks like. Hardware recommendations follow from that, not from a spec sheet.
Hardware procurement & build
We source and build to your spec — office-quiet CPUs for low-volume use, GPU-accelerated rigs for larger models or higher throughput. Lead time is typically 1–2 weeks after order.
Model installation & configuration
We install, quantize if needed, and configure the model for your use case. If you need a RAG pipeline tied to internal documents or a database, we wire that up here.
Network segmentation
The inference server sits on a dedicated VLAN, isolated from general office traffic. Access controls are set so only authorized machines reach the endpoint — or it's air-gapped entirely.
Handoff & documentation
You get a documented architecture, access credentials, and a walkthrough. Ongoing lifecycle support — model updates, rollbacks, change logs — available as a monthly add-on.
Common questions
Things people usually ask before moving forward.
Is it as capable as ChatGPT?
For most business tasks — summarizing documents, drafting emails, answering questions from your files — yes. The gap between cloud frontier models and capable local models has narrowed significantly. Where it matters: you own the context window, the data never leaves, and you're not dependent on a vendor's uptime or pricing changes.
What hardware do I actually need?
It depends on the model size and your throughput needs. A single-user office assistant for document Q&A might run on a $1,500–$2,500 mini PC. A shared endpoint for a 10-person team with larger models and faster response times might run $4,000–$8,000+ with GPU acceleration. We scope this per project — no one-size-fits-all number.
How long does setup take?
From signed scope to live system: typically 1–2 weeks after hardware arrives. The software side installs in hours. The bulk of the timeline is hardware procurement and shipping.
Can I connect it to my existing documents and databases?
Yes. RAG (Retrieval-Augmented Generation) pipelines let the model answer questions from your specific documents — contracts, policies, client files — without baking that content into the model itself. We build and maintain these pipelines as part of the deployment.
What happens when models improve?
We handle model updates as part of lifecycle support. You're not locked to whatever was current at install time. New model versions get tested, documented, and rolled in — with rollback procedures if something doesn't behave as expected.
HIPAA compliance requires organizational policies and business associate relationships in addition to technical safeguards. Our role is implementing the technical side — access control, encryption, auditing hooks, backup isolation — not providing legal attestations. For privilege, contractual confidentiality, or trade secret contexts, we scope architecture to the boundary you define, without replacing your counsel or compliance team.
Ready to scope a deployment?
Tell us what you're working with.
Share the workload — what data you need to work with, how many people will use it, and what your network looks like. We'll scope hardware and deployment from there. No obligation, no sales pitch.