$ localhost init
YourcompanyalreadyusesAI.Youjustdon'tcontrolityet.
The prototype worked.
The rollout didn't.
$999
Assessment to start
Your cloud
AWS · Azure · GCP · on-prem
0 bytes
Sent externally
running · isolated
transmitted externally
0 bytes
0 public endpoints
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 still pasting contracts into ChatGPT.
Your ops team built 14 AI workflows in Claude. None are in production.
Your Cursor prototype broke the moment 20 people used it.
Blocking ChatGPT isn't an AI strategy. It's a delay tactic.
Most internal AI projects fail at deployment. Not at prompting.
We productionize 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
→ production · isolated · 22 workflows running
0
bytes transmitted externally
0
public endpoints
0
internal workflows detected
0
third-party AI vendors
What actually happens
Week 1
2–3 days
Find every AI experiment your team already built.
Your engineers made demos. Your ops team has spreadsheet automations. Customer success has a Claude workflow nobody else knows about. We find all of it — the tools, the risk surface, and the three worth keeping.
Weeks 2–8
deployment
Turn the experiments worth keeping into production systems.
Private, access-controlled, audited. Running inside your environment. Not a ChatGPT wrapper. Actual infrastructure your team can rely on at scale.
After that
ongoing
Your team ships on top of it. Without breaking things.
Not because you told them to stop using public AI. Because the internal version is faster, safer, and actually works for more than one person.
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 team already built the prototype.”
Cursor got you started. Now you need infrastructure to make it usable by everyone — not just the person who built it.
“The demo worked. The rollout didn't.”
90% of internal AI failures happen between 'it works on my machine' and 'it runs reliably for 50 people.'
“You're not missing AI talent. You're missing AI infrastructure.”
Prompting is solved. Auth, audit logs, access control, uptime — that's where every internal project breaks.
“Three working workflows beats twenty abandoned experiments.”
Most teams have a graveyard of AI prototypes. We find the ones worth deploying and make them production-ready.
“Telling employees 'don't paste confidential data' is not a strategy.”
It's a liability. The risk surface has already outrun your policy.
“The companies that compound win.”
Each production workflow teaches you the next one. The gap between you and competitors widens with every deployment.
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
From AI experiments
to production systems.
Your team built the experiments.
Most internal AI fails between the demo and the rollout.
That's exactly where we start.