A Mac Mini M4 Pro (or any hardware) running Ollama, fine-tuned on your documents, accessible through Open WebUI, with API endpoints for Teams, SharePoint, and custom apps. Enterprise AI for under $7,000. No cloud. No data leaks. No per-seat fees. No vendor lock-in.
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Every query your employees type into ChatGPT, Gemini, or Claude goes through external servers. Trade secrets, customer data, financial projections, legal strategies, HR records -- all of it passes through infrastructure you do not control. OpenAI's enterprise tier claims data isolation, but their terms of service still grant broad usage rights, and you have no way to verify what happens to your data after it leaves your network. For organizations subject to HIPAA, SOC 2, FINRA, or state privacy laws, this is not a theoretical risk -- it is an active compliance violation.
ChatGPT Enterprise costs $60/user/month. At 100 users, that is $72,000/year. At 500 users, $360,000/year. And the price only goes up. Microsoft Copilot for M365 adds $30/user/month on top of your existing E3 or E5 license, and most organizations report less than 40% adoption after the first 90 days because the tool does not know their specific processes, policies, or terminology. You are paying enterprise rates for a generic assistant that gives generic answers.
Cloud AI does not know your PTO policy, your deployment runbooks, your product specifications, your compliance requirements, or your customer history. It gives generic answers to specific questions. Your employees waste time reformulating prompts, providing context that should already be embedded in the model, and manually verifying answers against internal documentation. A private LLM fine-tuned on your actual documents answers correctly the first time because it has already ingested your institutional knowledge.
The open-source AI ecosystem has matured dramatically. Here are the models we deploy in production environments today, matched to specific use cases and hardware requirements.
| Model | Parameters | Best For | Min. Hardware | Our Rating |
|---|---|---|---|---|
| Llama 3.1 405B (quantized) | 405B (Q4) | General knowledge, reasoning, code generation | Mac Studio M4 Ultra 192GB | Best overall |
| Llama 3.1 70B | 70B | Knowledge base Q&A, document analysis, drafting | Mac Mini M4 Pro 48GB | Best value |
| Mistral Large 2 | 123B | Multilingual, legal/compliance, long-context analysis | Mac Studio M4 Max 128GB | Best multilingual |
| Phi-3 Medium | 14B | Lightweight tasks, edge deployment, rapid inference | Mac Mini M4 24GB | Best speed |
| Gemma 2 27B | 27B | Summarization, customer support, structured output | Mac Mini M4 Pro 36GB | Best structured output |
| Qwen 2.5 72B | 72B | Code generation, math, technical documentation | Mac Mini M4 Pro 48GB | Best for code |
We do not pick a model and force it on every client. During the assessment phase, we evaluate your specific use cases -- knowledge base Q&A, document analysis, code generation, customer support drafting, compliance checking -- and recommend the model that performs best on your actual data. For most mid-market organizations with 50-200 employees, Llama 3.1 70B running on a Mac Mini M4 Pro delivers the optimal balance of quality, speed, and cost. For organizations that need multilingual support or long-context analysis (contracts, regulatory documents), Mistral Large 2 on a Mac Studio is the better choice. We benchmark every recommendation against your real questions before deployment.
A complete deployment stack from hardware to user interface, all running on your premises with zero cloud dependency.
Apple Silicon's unified memory architecture is purpose-built for large language model inference. The M4 Pro with 48GB unified memory can load and run a 70-billion parameter model entirely in memory, delivering 30-50 tokens per second -- fast enough for real-time conversational AI. The entire unit is smaller than a textbook, uses 40 watts of power (less than a light bulb), produces zero fan noise at idle, and costs under $2,000. For larger models or higher concurrency, we deploy Mac Studio M4 Max (128GB) or M4 Ultra (192GB) configurations. For organizations with existing server room infrastructure, we also support Dell PowerEdge and Lenovo ThinkSystem rack servers with NVIDIA GPU acceleration.
Ollama is the inference engine that loads, manages, and serves your language models. It handles model quantization (reducing model size while preserving quality), GPU memory management, context window configuration, and concurrent request handling. Ollama runs as a background service on the Mac Mini, starts automatically on boot, and exposes a local API on port 11434. We configure Ollama with optimal quantization settings for your specific model -- typically Q5_K_M for the best quality-to-speed ratio -- and set up automatic model switching for organizations that run multiple models for different use cases.
We ingest your organization's documents, policies, runbooks, product specifications, customer FAQs, and institutional knowledge into a Retrieval-Augmented Generation (RAG) pipeline. This is not traditional fine-tuning (which modifies model weights) -- it is a retrieval layer that indexes your documents and injects relevant context into every query. The result: your AI answers questions about your specific business with citations to the source document. We use ChromaDB or Qdrant as the vector database, running locally on the same hardware, with automatic re-indexing when documents are updated.
Open WebUI provides a ChatGPT-style web interface that your team already knows how to use. It supports conversation history, document upload, image analysis (with multimodal models), user management with role-based access control, and custom system prompts per department. The interface is accessible from any device on your network -- laptops, tablets, phones -- through a standard web browser. No client software installation required. We configure SSO integration with your existing Microsoft Entra ID (Azure AD) so users authenticate with their corporate credentials.
The local API (OpenAI-compatible format) enables integration with Microsoft Teams (bot that answers questions in channels), SharePoint (AI-powered search across your document libraries), Power Automate (AI steps in your existing workflows), custom internal applications, and helpdesk systems like ServiceNow or Freshdesk. We build and deploy these integrations as part of the engagement, not as a separate project. The API is secured with token-based authentication and rate limiting, accessible only from your internal network.
M4 Pro with 48GB unified memory runs 70B parameter models at 30-50 tokens/second. Smaller than a textbook, quieter than a whisper, uses less power than a light bulb.
RAG pipeline indexes your documents, policies, runbooks, and product specs. Every answer cites its source document. Automatic re-indexing when documents change.
Open WebUI with SSO via Entra ID, conversation history, document upload, role-based access. Accessible from any device on your network through a standard browser.
OpenAI-compatible API for Teams bots, SharePoint AI search, Power Automate workflows, ServiceNow integration, and custom applications. Token auth, rate limiting, internal-only access.
Llama 3.1, Mistral Large 2, Phi-3, Gemma 2, or Qwen 2.5 -- open-source models with no vendor lock-in. Switch models anytime. The weights live on your hardware.
One-time deployment cost under $7,000. No per-seat charges, no API usage fees, no annual renewals. 100 users costs the same as 1,000 users: $0/month.
"What's our PTO policy?" "How do I submit an expense report?" Instant answers from your own documentation, with citations to the source document.
Upload contracts, proposals, or reports. Get summaries, key clauses, risk flags, and action items in seconds. All processing happens on your hardware.
Auto-generate response templates from your knowledge base. Consistent tone, accurate product information, faster resolution times across every support channel.
"Why is this deployment failing?" Your AI knows your runbooks, your error codes, your environment-specific fixes. L1 support becomes L2 overnight.
Validate documents against HIPAA, SOC 2, FINRA, or your internal regulatory requirements. Flag non-compliant language automatically. Zero data leaves your building.
New hires get answers without bothering the team. Day-one productivity with AI that knows every policy, procedure, and institutional practice.
| Metric | ChatGPT Enterprise | Microsoft Copilot | AI in a Box |
|---|---|---|---|
| Cost (100 users, Year 1) | $72,000 | $36,000 + E3/E5 license | $6,900 one-time |
| Cost (100 users, Year 3) | $216,000 | $108,000+ | $6,900 total |
| Data Residency | OpenAI servers | Azure (shared tenant) | Your hardware, your building |
| Custom Training | Limited GPT builder | No custom models | Full RAG + fine-tuning |
| API Access | Separate billing | Limited Graph API | Unlimited, included |
| Vendor Lock-in | High | High | None -- open-source models |
| HIPAA Compliant | BAA available, data leaves network | BAA available, shared infra | Full -- data never leaves premises |
What questions does your team need answered? What documents should it know? We map your AI use cases, benchmark candidate models against your actual questions, and recommend the optimal hardware-model combination.
Hardware provisioning, Ollama configuration with optimal quantization, RAG pipeline setup with your document corpus, Open WebUI deployment with Entra ID SSO. We validate accuracy against 50+ test questions from your actual use cases.
Teams bot deployment, SharePoint AI search connector, Power Automate workflows, API endpoints for custom apps. We build every integration as part of the engagement -- not as a future phase or upsell.
Team training on prompt best practices, guardrail configuration, document management, and ongoing model updates. Your AI gets smarter as your documentation grows. We provide runbooks for common maintenance tasks.
Because it uses RAG (Retrieval-Augmented Generation) on your actual documents, accuracy matches the quality of your source material. Every answer includes citations to the source document so your team can verify. We benchmark accuracy against 50+ real questions during deployment and tune retrieval parameters until answer quality meets your standards.
All LLMs can generate incorrect information. We implement multiple guardrails: citation requirements (every answer must reference a source document), confidence scoring (low-confidence answers are flagged), retrieval thresholds (if no relevant document is found, the AI says "I don't know" instead of guessing), and human-in-the-loop workflows for high-stakes queries. RAG significantly reduces hallucination rates compared to bare model inference.
No. It runs entirely on local hardware with no internet dependency. This is ideal for air-gapped environments, classified networks, or organizations with strict data residency requirements. Model updates are performed via USB transfer for fully air-gapped deployments.
Model updates and document re-indexing are included in our Secure+ managed services tier ($500/month). For one-time deployments, we offer periodic refresh engagements to incorporate new documents, swap to improved base models (the open-source landscape evolves rapidly), and tune retrieval parameters based on usage analytics.
Microsoft Copilot for M365 costs $30/user/month, runs on shared Azure infrastructure, cannot be fine-tuned on your custom documents, and has limited API access. AI in a Box costs $6,900 one-time for unlimited users, runs on your own hardware, is fully customizable with RAG and fine-tuning, and includes unrestricted API access. For organizations that need M365-embedded AI features (like AI in Word or Excel), Copilot is complementary -- but for knowledge base Q&A, document analysis, and custom workflows, AI in a Box is significantly more capable and economical.
AI in a Box is the foundation. These services extend your AI capability across the organization.
Autonomous AI that takes actions, not just answers questions
Policies, guardrails, and compliance for enterprise AI
Get your team from skeptical to productive
Workflow automation powered by your private LLM
Private code assistant for your engineering team
Natural-language queries on your business data
Free 30-minute AI assessment. We map your use cases, benchmark models against your actual questions, and show you exactly what a private LLM deployment looks like for your organization.