// AI TIER 01 · SANDBOX

Private AI Deployment

Private AI Sandbox & Knowledge Base

Zero data-leakage AI for professional service firms.

For law offices, medical clinics, accounting firms, and real-estate teams that need secure, offline AI search across internal documents — with no data leakage risk to third-party APIs.

// WHO.THIS.IS.FOR

Fit criteria.

+Professional service firms with confidential client data
+Practices bound by HIPAA, attorney-client privilege, or fiduciary duty
+Small firms (3–30 people) that currently rely on ChatGPT for internal Q&A
+Any team that has quietly banned staff from pasting client data into cloud AI

// WHAT.INCLUDED

Scope of work.

Hardware Setup

Installation and performance tuning on client's existing hardware (Mac Studio, or workstation with 32GB+ unified memory or 16GB+ VRAM GPU).

Model Engine

Quantized 7B–14B open-weight models (Llama 3 / Qwen / Mistral) optimized for local inference speed on your hardware profile.

Document RAG System

Local vector database (Qdrant or Chroma) connecting to internal PDFs, manuals, standard operating procedures, and legacy archives.

User Interface

Web-based team chat interface (Open-WebUI or AnythingLLM) with role-based access control.

Onboarding

Live staff training session + customized system prompt playbook tailored to your practice's language and workflows.

Monthly Support Retainer

Ongoing maintenance, model updates, prompt refinements, and system support.

// HOW.WE.DELIVER

Delivery cadence.

Discovery (Week 1)

Hardware audit, document scope survey, security requirements sign-off.

Build (Weeks 2–3)

Local model install, RAG pipeline, UI setup, index of your document corpus.

Cutover (Week 4)

Staff training, prompt playbook handoff, sign-off. Retainer begins.

// DELIVERABLES

What ships.

→Fully configured local AI stack
→Indexed document knowledge base
→Role-based user interface
→System-prompt playbook document
→Ongoing retainer coverage

// OUTCOMES

What you should expect.

↗Staff stop pasting confidential data into cloud AI tools
↗Instant answer retrieval across internal documents
↗Auditable AI usage inside your firewall

// 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

$750 / mo

Plus one-time build fee: $5,000 – $8,500

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.

Fully configured local AI stack
Indexed document knowledge base
Role-based user interface
System-prompt playbook document

Typical payback trigger: staff stop pasting confidential data into cloud ai tools.

// 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.

What if we don't have suitable hardware?

We spec and source it as part of scoping. Typical setup is a single Mac Studio or workstation — well under $10k.

How is this different from ChatGPT Enterprise?

Nothing leaves your building. No data is sent to OpenAI or any third-party API. Complete air-gapped operation.

// RELATED.SERVICES

AI Risk & Compliance Audit

AI Readiness & Vulnerability Assessment

$3,500 – $5,000

"Shadow AI" scan + safe adoption roadmap.

Private AI Deployment

Enterprise On-Prem Node & Multi-Agent Engine

$1,800 / mo

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

// NEXT.STEP

Scope AI Sandbox.

Founder-sponsored scoping. No sales-team gauntlet.

all services [ Scope AI Sandbox ]