Private AI Infrastructure
AI That Never Leaves
Your Environment.
We design and deploy private AI infrastructure, self-hosted models and systems that keep your data inside your environment, for teams that can't send sensitive data to a third-party API.
Why Private AI
Public APIs Aren't an Option for Every Workload
For a lot of enterprise data, sending it to a third-party API isn't a technical decision, it's a legal one.
Where It Breaks
How We Build It
“Our data can't leave our environment, full stop.”
We deploy models directly inside your infrastructure, cloud VPC, on-prem, or air-gapped, so nothing is sent to a third-party API.
“Legal won't approve sending customer data to a public model API.”
We build on self-hosted open-source models where data residency requirements rule out public APIs.
“We don't have the infrastructure team to run our own models.”
We handle the infrastructure, provisioning, scaling, and maintaining the models, so your team doesn't have to.
“We need AI, but our industry's regulations make most vendors a non-starter.”
We've deployed private AI for regulated industries: healthcare, finance, government contractors, where compliance rules out standard SaaS AI tools.
“Latency and cost of third-party APIs are killing our margins at scale.”
Self-hosted infrastructure gives you predictable cost and latency at volume, without per-token API pricing.
What We Deploy
Private AI Infrastructure, Fully Managed
From model selection to ongoing operation, we run private AI infrastructure as part of the engagement, not a one-time handoff.
Self-Hosted Model Deployment
Open-source and licensed models deployed inside your cloud VPC, on-prem servers, or air-gapped environment.
Infrastructure Setup & Scaling
GPU provisioning, inference optimization, and autoscaling configured for your workload and budget.
Private AI Assistants & Agents
Internal copilots and agents that run entirely on your private infrastructure, with no data sent externally.
Ongoing Management
Monitoring, model updates, and scaling handled as part of the OPERATE phase, not a one-time deployment.
Deployment Model
Three Phases.
One Outcome: AI in Production.
/ 01
AI AUDIT
1–2 weeks
Our engineers integrate with your team to map current workflows, identify AI leverage points, and produce a concrete build plan with scope, timeline, and expected outcomes.
- —Workflow mapping sessions
- —AI opportunity scoring
- —Delivery roadmap
/ 02
BUILD
4–8 weeks
Engineering begins. We build, test, and iterate on production-ready AI systems against your actual data and real operational conditions.
- —Custom model pipelines
- —Integration with existing stack
- —Iterative testing in staging
/ 03
OPERATE
Ongoing
We operate the system alongside your team, monitor performance, resolve edge cases, and expand capabilities as your operations scale.
- —Live operations support
- —Performance monitoring
- —Ongoing iteration
FAQ
Frequently Asked Questions
Everything you need to know about our AI systems, how we build, and what to expect.
Start Here
Stop sending data out.
Start keeping AI in-house.
One 30-minute call to scope a private AI deployment that never sends your data to a third-party API. No commitment, no deck, just a plan.
30 min · No commitment