01
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
My AI
Local intelligence that replaces cloud-scale spend.
Frontier models in the cloud are expensive, leaky, and overkill for most agentic work. We put capable models on your hardware — and keep them upgraded for you.
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 — not the marketing brochure.
Security by default
Your data stays on hardware you control.
Platform
What we put on your floor
End-to-end local AI: models, orchestration, and the agents that keep everything current.
02
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
03
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
04
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.
Providers argue you should stay in the cloud because “someone else handles upgrades.” That argument aged poorly. AI maintenance is now automatable — and we run that automation 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.