Private AI Appliance

On-Site AI. Local by Default.

Hardware in your building, running local inference — with a routing policy that only reaches out to the cloud when a task genuinely needs it, visibly rather than silently.

Why On-Prem

For Data That Shouldn't Leave the Building

Some businesses cannot or will not send certain information to a third-party model — customer records, financials, anything under a contractual or regulatory constraint. This offering exists for exactly that case, without giving up AI assistance entirely.

Local by Default

No data leaves the building unless the routing policy says so.

Hybrid Routing

Cloud models handle only the tasks that genuinely need them.

Optional Voice

A voice interface for structured, everyday requests.

Pricing

$8K–$20K

plus hardware

  • On-site install of local inference hardware
  • Hybrid routing policy — local by default, cloud only when justified
  • Optional voice interface
  • No data leaves the building unless the routing policy says so
  • Install runbook and a scoped SOW, not an open-ended retainer

Visible, Not Silent

How Hybrid Routing Actually Decides

The same routing discipline we sell is the one we run. Every request is handled by the simplest capable model — and which one handled it is never a mystery.

Example RequestHandled By
Looking up a customer record or internal document
Local
Drafting a routine internal email or summary
Local
Complex reasoning over a long, unfamiliar document
Cloud (visible)
Voice command to check status or trigger a known workflow
Local

The exact policy is configured per install — what counts as sensitive, which tasks are allowed to escalate, and what happens when the local model is uncertain are all decisions we make with you, not for you.

Reference Architecture

Hardware Specification

The install we productized: Mac Mini–class host with local models, integrated backup, and optional voice edge. This is the documented reference, not a locked SKU — we configure around your existing infrastructure when it makes sense.

Primary Host (Reference)

Platform
Mac Mini M4 Pro
Memory
24GB RAM
Internal storage
512GB SSD
External backup
2TB SSD (dock)

Always-on headless server. SSH and LAN-bound inference. The 512GB internal houses runtime; the 2TB external handles backups, exports, and archives.

Internal Space Budget

macOS + apps
~80–120GB
Local models (1–2)
~15–40GB
Runtime memory
Grows over time
Working headroom
≥100GB free

Heavy archives and model archives live on the external drive when space tightens. Runtime stays on internal for speed.

Optional Voice Edge

For voice-controlled workflows, a Raspberry Pi 4 running Home Assistant OS with Wyoming add-ons handles speech-to-phrase and text-to-speech locally, forwarding conversation to the Mini's LAN-bound model. Not required for the core install; add only if you need hands-free, voice-first interaction.

Speech processing stays on the Pi; nothing leaves the LAN unless the routing policy allows it.

Implementation Path

Install Milestones

The documented runbook, organized into customer-facing phases. Each step builds on the last; the whole install is reproducible and version-controlled as config-as-code.

1

Hardware Prep

macOS config for always-on headless operation, SSH access, Homebrew, external backup volume mounted and formatted.

2

Local Inference + Agent

LM Studio with a 24GB-appropriate model (65k context), Hermes Agent installed with SOUL/USER config, first conversation verified.

3

Hybrid Routing Config

Cloud model keys wired (Nous Portal + direct providers), routing policy documented and live, usage tracking enabled.

4

Messaging Interface

Slack or Telegram gateway set up, launchd service for auto-restart on crash/reboot, first mobile message round-trip confirmed.

5

First MCP Integrations

Filesystem, git, and at least one business tool (e.g. GitHub) wired and verified — the foundation for tool orchestration.

6

Backups & Always-On

Nightly rsync to the external drive via launchd, rotating retention, manual restore tested. The hub survives a full internal wipe.

7

Optional Voice

Pi4 with Home Assistant OS, Wyoming speech add-ons, LM Studio bound to the LAN, first voice-to-action round-trip working.

Tool Orchestration

Integration Catalog

MCP (Model Context Protocol) unifies tool access across business systems, smart building, and memory — one routing layer instead of one-off integrations per tool.

Core

  • Filesystem (local workspace, config repos)
  • Git (status, diff, log, push/pull automation)

Business

  • GitHub (issues, PRs, repo file access)
  • Slack (send, schedule, search channels)
  • Google Workspace (Gmail, Calendar, Drive, Tasks)
  • QuickBooks (invoices, customers, read-only by default)
  • Vercel (deployments, logs, analytics)

Smart Building (Advisory-Only)

  • Home Assistant (read device state, suggest actions — never writes to devices unattended)

Device control stays strictly advisory. The agent can flag and suggest, never execute a state change without explicit approval.

Memory & Continuity

  • Memory server (cross-session knowledge graph, persisted locally)
  • ME profile (personal facts, preferences, durable identity)

Models are interchangeable workers; the brain lives in MCP memory, not any one model's context. Switch models without losing continuity.

Routing Discipline

Sensitive-Local-By-Default Policy

Which tasks stay on-device and which escalate to cloud models — the documented policy generalized from our own hub install for SMB use cases.

Task Involves…Route To
Client data, consulting records
Local
Financial data (invoices, QuickBooks)
Local
Personal PII
Local
Hard reasoning, large context, no sensitive data
Cloud (visible)
Uncertain / mixed
Ask first

The exact definitions (what counts as "sensitive," which tasks may escalate, whether the local model should ask before routing uncertain cases) are decisions we document with you during install, not defaults we impose.

Proven on Our Own Infrastructure First

This is not a vendor pitch for hardware we have never run. The install runbook, routing policy, and tool-integration catalog behind this offering come directly from our own on-site AI hub — see the case study for how it actually works day to day.

Read the Hermes Hub case study

Curious whether on-prem makes sense for you?

Most businesses don't need it. If yours handles data that genuinely should not leave the building, let's talk about whether this is the right fit.