$ localhost init
YourcompanyalreadyusesAI.Youjustdon'tcontrolityet.
Private AI. Inside your environment.
Your employees already solved the adoption problem.
$999
Assessment to start
Your cloud
AWS · Azure · GCP · on-prem
0 bytes
Sent externally
$ localhost deploy --org acme-corp
Your AI is live.
ai.acme-corp.internal
0 public endpoints detected.
0 bytes transmitted externally.
All data stayed in your environment.
The situation
Your legal team is pasting contracts into ChatGPT.
Your interns uploaded something confidential last week.
IT has identified 14 AI tools in use. Zero are approved.
Blocking ChatGPT isn't an AI strategy. It's a delay tactic.
You're about to post a $175K AI Director job to figure this out.
We fix it. Workflow by workflow.
What we build
Your AI stack.
Owned by you.
Pre-built patterns tailored to your workflows and risk surface.
# runs in your environment
# 0 public endpoints
# audit log on every query
Ask it anything your company knows.
A private AI assistant trained on your documents. Answers like a senior employee who has read everything. No external access, ever.
Find the contract nobody remembers.
Semantic search across internal docs, contracts, and policy libraries. Natural language. Cross-source. Nothing leaves.
Draft it. Review it. Never paste it into ChatGPT.
AI drafting, clause review, and summarization for contracts and proposals — running entirely inside your environment.
Automate the repetitive stuff nobody wants to touch.
Intake, classification, routing, reporting — automated without touching a public model. Audit logs included.
Stop re-explaining things your company already knows.
Capture institutional knowledge. Surface it instantly when your team needs it. Connects to SharePoint, Drive, and internal wikis.
Each engagement starts with an assessment — we scope only what makes sense for your environment, not what looks good in a proposal.
Why private
Public AI SaaS
Your data leaves
every time someone types.
- —Data enters their training pipeline
- —Compliance becomes your problem to solve
- —Vendor controls the model, uptime, and pricing
- —One confidential paste. One incident report.
- —Audit logs: minimal or nonexistent
Localhost Private
AI that runs where
your data already lives.
- ✓Data stays in your environment. Completely.
- ✓Designed for regulated industries from day one
- ✓Open-source model. You own it. No lock-in.
- ✓Isolation enforced at the infrastructure level
- ✓Full query logs, access control, and history
Works with AWS, Azure, GCP, or on-premises. Open-source models only — no API keys to OpenAI, Anthropic, or any third party.
$ localhost status --org acme-corp
→ running · isolated · last sync 2 min ago
0
bytes sent externally
0
public endpoints
0
documents indexed
0
third-party vendor APIs
What actually happens
Week 1
2–3 days
Find every unofficial AI workflow already running inside your company.
Your team found ChatGPT months ago. They've built habits around it. We surface all of it — the tools, the workflows, the risk surface. Every single one.
Weeks 2–8
deployment
Deploy private replacements inside your environment.
Your data doesn't move. The capability does. Everything runs where it already lives. From chaotic to controlled in under 60 days.
After that
ongoing
Your team stops leaking data into public AI.
Not because you told them to. Because they don't need to anymore.
What companies figure out
Things we hear
on every first call.
Not opinions. Patterns. Every company we've talked to was already here before they called us.
“Your employees aren't waiting for IT approval.”
They're using Claude, ChatGPT, and Copilot right now. With your data.
“Blocking ChatGPT doesn't stop AI usage.”
It makes it invisible. Which is worse.
“Most teams don't need an AI department.”
They need three workflows that actually work. That's a very different problem.
“The problem isn't the model. It's where it runs.”
Any frontier model can be self-hosted. We've done it dozens of times.
“Telling employees 'don't paste confidential data' is not a strategy.”
It's a liability. Your policy hasn't caught up with your risk surface.
“The companies that move first don't just automate. They compound.”
Each workflow deployed teaches us the next one. The gap widens.
How to begin
Starts with a
private AI assessment.
Two to three days. We audit your workflows, map your risk surface, and tell you exactly what to build — and what to skip. No generic roadmaps. No 40-page decks.
$999
flat fee
2–3
days to complete
Yours
to keep
Most teams continue to a full deployment. No obligation to.
What you get
$ localhost assess --org your-company
→ scanning workflows...
→ results in 2–3 days
$ localhost deploy --env production
AI that runs
where your data lives.
Most companies don't need more AI tools.
They need control of the ones already running.