My AI

We build local, purpose-built, model→task infrastructure.

Save thousands per year with stacks sized to the job — not rented frontier APIs. See the ROI use case below.

Start a conversation See the ROI use case →

70–90% Typical inference cost reduction vs. frontier APIs

0 Customer prompts leaving your network

24/7 Automated upgrade & security agents

The shift

Same work. A fraction of the bill.

Businesses don’t need to rent frontier models for every workflow. Smaller local models now complete the same agentic tasks — drafting, routing, classifying, summarizing, tool-calling — with lower cost and data that never leaves the building.

Cloud vendors sell “managed upgrades so you can focus on the business.” In AI today, that promise is inverted: model updates, patches, and security hardening can run automatically. Our agent infrastructure does the maintenance on your local stack.

Right-sized models Match the job.

Security by default Your data stays on hardware you control. We provide the required hardware and installation — you just use an internal service.

Platform

What we put on your floor

End-to-end local AI: models, orchestration, and the agents that keep everything current.

Local model stacks

Right-sized open models for the jobs you actually run — not a rented frontier for every token.

On-prem & private VPC inference
Agentic tool use & RAG
Workload-tuned quantization

Security by architecture

Sensitive documents, customer data, and internal tools stay inside your boundary.

No training on your traffic
Air-gap ready deployments
Policy-aware access controls

Autonomous upkeep

Our agents watch for model, runtime, and CVE updates — then apply them on schedule.

Model rollouts with rollback
Security patch pipelines
Health & capacity monitoring

Migration from cloud agents

We map your existing frontier workflows and move them local without a rewrite from scratch.

Flow audit & model matching
Side-by-side quality gates
Cutover with cost dashboards

Maintenance, reinvented

Cloud promised a team. We ship agents.

We use agents to update the local stack — model releases, patches, and security hardening run on the machines you own.

Observe

Watch the stack

Agents track model releases, runtime CVEs, driver changes, and latency / error budgets across your nodes.

Decide

Score the change

Each update is evaluated for quality, risk, and cost. Only changes that pass your policy move forward.

Apply

Patch without drama

Rolling upgrades, canaries, and automatic rollback keep production agent flows online while security stays current.

Use case

From frontier cloud to local ROI

A mid-market ops team runs an agentic support & document workflow — triage tickets, draft replies, extract fields, call internal tools — previously powered by frontier APIs in the cloud.

Before — frontier cloud

API-metered agents

Volume: ~45M tokens / month
Model mix: Frontier chat + tools
Monthly inference: $28,000
Data posture: Leaves the network

After — My AI local

Right-sized on-prem models

Volume: Same workflows
Model mix: Local 7B–70B agents
Monthly run cost: ~$3,400
Data posture: Stays on hardware

~$295k / year Estimated inference savings after hardware amortization

~88% Lower monthly run cost

<5 mo Payback on local GPU nodes

Illustrative composite based on publicly listed frontier API rates vs. typical local inference + power for a comparable agentic load. Your numbers vary — we’ll model yours.

Next step

Let’s put AI back on your balance sheet.

Tell us about the workflows you’re running in the cloud. We’ll map a local path, the hardware footprint, and the payback window.