// AI TIER 02 · ON-PREM
Private AI Deployment

Enterprise On-Prem Node & Multi-Agent Engine

Mid-market private AI for 50–200 person orgs.

For mid-market companies looking to replace high-volume cloud API fees with an internal, multi-department private AI infrastructure that scales across teams — without vendor lock-in.

// WHO.THIS.IS.FOR

Fit criteria.

  • +50–200 employee companies with cross-department AI needs
  • +Firms whose cloud AI bills have crossed $2k/mo and are still growing
  • +IT organizations required to keep data on-prem
  • +Teams building multiple internal AI-assisted workflows
// WHAT.INCLUDED

Scope of work.

Hardware Architecture
Dedicated on-premise workstation or rack-server configuration (Dual RTX 4090, Quad RTX 6000, or Mac Studio cluster).
Model Engine
Multi-model pipeline running 32B–70B parameter open-weight models with dynamic task routing per workload.
Deep RAG Integrations
Direct indexing hooks for SharePoint, Google Drive, Notion, local NAS — with automated document re-indexing on change.
Agentic Workflows
Custom AI agents tailored per internal team: sales-ops agent, technical-docs agent, marketing-content agent.
Security & Governance
Local API deployment, audit logging, PII masking, and role-based data permissions across teams.
SLA Support
Monthly retainer covers SLA support, model updates, and quarterly context-engineering sprints.
// HOW.WE.DELIVER

Delivery cadence.

01
Architecture (Weeks 1–2)

Hardware spec, security review, RAG integration mapping, agent scoping.

02
Build (Weeks 3–6)

Hardware provisioned, models deployed, RAG pipelines connected, agents scaffolded.

03
Rollout (Weeks 7–8)

Team-by-team enablement, playbooks issued, retainer support begins.

// DELIVERABLES

What ships.

  • Production-grade on-prem AI stack
  • Integrated RAG across file systems
  • 3+ custom departmental AI agents
  • Audit + governance logs
  • SLA-backed monthly retainer
// OUTCOMES

What you should expect.

  • 60–90% reduction in cloud AI API spend
  • Institution-wide AI availability without data risk
  • Compounding productivity as agents mature
// 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
$1,800 / mo
Plus one-time build fee: $15,000 – $28,000
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.

  • Production-grade on-prem AI stack
  • Integrated RAG across file systems
  • 3+ custom departmental AI agents
  • Audit + governance logs

Typical payback trigger: 60–90% reduction in cloud ai api spend.

// 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.

How does this compare to Copilot / Enterprise ChatGPT costs?

For orgs with 50+ AI users, the on-prem TCO breaks even in 12–18 months — and then compounds savings indefinitely.

What happens if a model gets deprecated?

Because we deploy open-weight models on your hardware, nothing gets deprecated on someone else's timeline. Upgrades are your call.

// NEXT.STEP

Scope Enterprise On-Prem.

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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