// AI TIER 03 · APPLIANCE
Private AI Deployment

Turnkey On-Prem Appliance & Custom Fine-Tuning

For regulated enterprises where cloud AI is a non-starter.

For highly regulated enterprises — healthcare networks, financial institutions, defense contractors — requiring air-gapped, zero-cloud operational environments with proprietary fine-tuned models on private data.

// WHO.THIS.IS.FOR

Fit criteria.

  • +Healthcare networks bound by HIPAA and BAAs
  • +Banks and insurers under SOC2 / regulator scrutiny
  • +Defense and gov contractors with air-gap mandates
  • +Enterprises with proprietary datasets that materially differentiate their outputs
// WHAT.INCLUDED

Scope of work.

Custom Data Pipeline
Full data sanitization, formatting, and dataset labeling on your historical data. This is typically 30–50% of the build effort.
Domain Fine-Tuning
LoRA / QLoRA fine-tuning of 13B–70B open-weight models on your proprietary corpus and task specifications.
Air-Gapped Infrastructure
Complete offline enterprise deployment with hardware acceleration via vLLM / TensorRT-LLM backends.
Enterprise Integration
Custom webhooks, Model Context Protocol (MCP) integrations into internal ERPs, CRMs, and business applications.
Governance & Compliance
Full HIPAA / SOC2 compliance documentation, local vulnerability scanning, and dedicated SLA support.
MLOps
Dedicated MLOps oversight, model-drift evaluation, and quarterly retraining cycles as your data grows.
// HOW.WE.DELIVER

Delivery cadence.

01
Diligence (Weeks 1–3)

Compliance review, data audit, hardware sizing, fine-tuning scope-lock.

02
Data engineering (Weeks 4–10)

Sanitization, labeling, evaluation-set construction.

03
Fine-tune + integrate (Weeks 11–16)

Model training, integration with internal systems, red-team validation.

04
Cutover + MLOps

Production deployment, monitoring, retraining cadence begins.

// DELIVERABLES

What ships.

  • Air-gapped enterprise AI appliance
  • Fine-tuned proprietary model(s)
  • Full compliance documentation package
  • Enterprise system integrations
  • MLOps retainer coverage
// OUTCOMES

What you should expect.

  • Regulator-defensible AI deployment
  • Proprietary model advantage no cloud vendor can replicate
  • Zero dependency on third-party AI infrastructure
// WHY.THIS.MATTERS

Why Private AI Deployment matters right now.

Enterprise AI is moving from experiment to infrastructure. Every quarter you delay a private/on-prem deployment, your data leaves your control and your compliance surface widens.

Cost of doing nothing

Public API tools leak proprietary data. Employees paste customer info into ChatGPT daily. Every leaked prompt is a potential regulatory event and a competitive advantage handed to your rivals.

// COST.VS.VALUE

Cost vs. value — do the math.

your investment
$3,500 – $5,000 / mo
Plus one-time build fee: $45,000 – $85,000+ (excludes hardware)
comparable market rates
$150k–$500k+ to build in-house with a 2-engineer AI team over 6–12 months
value delivered

What you actually get.

  • Air-gapped enterprise AI appliance
  • Fine-tuned proprietary model(s)
  • Full compliance documentation package
  • Enterprise system integrations

Typical payback trigger: regulator-defensible ai deployment.

// WHY.NOW

Why now — not next quarter.

The AI infra window is closing. Enterprises deploying now lock in the operational advantage. Late movers pay 3–5x more, take twice as long, and inherit legacy pain.

typical timeline
Immediate scope confirmation.
Start now
// FAQ

Common questions.

Do you help procure hardware?

Yes — we spec and source, but the physical hardware is billed directly to you at cost.

Can we run this without a full-time MLOps hire?

That's what the monthly retainer is for. We own drift monitoring, evals, and retraining.

// NEXT.STEP

Book Discovery Call.

Founder-sponsored scoping. No sales-team gauntlet.

TraceElements

Intelligent infrastructure. Built by the architects, not middlemen.

// Signal

Status: Operational

v1.0.0 · build 2026

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