Private AI: How Companies Use ChatGPT-Style Tools Without Sharing Their Data

A private AI assistant gives your team ChatGPT-style help (drafting, summarizing, answering questions from your own files) while running on servers you control. Your data never goes to a public AI company. For businesses held back by client contracts, regulations, or internal policy, it's how they finally get to use AI at work.

Why many companies still can't use public AI

When an employee pastes a contract, a patient note, or a client's financials into a public AI tool, that data leaves the company's control. For a lot of businesses, that's a dealbreaker:

• Client contracts that forbid sharing client data with third parties
• Regulated data: health records, financial data, legal files, government work
• Trade secrets and source code that can't leave the building
• Audit requirements: needing a record of who accessed what

Some of the biggest names in business reacted the same way. Samsung restricted ChatGPT in 2023 after staff pasted internal code into it. JPMorgan Chase and others restricted public AI tools early on, and many have since built internal AI assistants with their own guardrails.

The problem with a ban is that people still need the help. Without an approved tool, staff either lose hours or quietly use public AI anyway. Neither is good.

What "private AI" actually means

A private AI setup has four parts, all inside your control:

1. The model. Open-weight AI models (ones you can download and run yourself) have improved dramatically. Models from Meta, Mistral, DeepSeek, Alibaba's Qwen, and OpenAI's own open-weight release can handle most day-to-day business work.
2. Where it runs. On your own server, in your private cloud account (AWS, Azure, Google Cloud), or fully offline on an isolated network.
3. Your knowledge. The assistant is connected to your documents, policies, and systems, so it answers from your information and can cite where an answer came from.
4. Access control. Single sign-on, user permissions, and activity logs that match your compliance rules.

The result looks and feels like ChatGPT to your team. The difference is where the data goes: nowhere.

What teams use it for

• "Ask our documents." Policies, contracts, SOPs, past proposals, and product manuals, answered in seconds with sources.
• Drafting. Proposals, reports, client emails, and job descriptions, in your company's voice.
• Summarizing. Long contracts, claims, meeting notes, and case files.
• Data questions. "Summarize open warranty claims over $10K this quarter" across spreadsheets and exports.

Private vs. public AI: the trade-offs

Business tiers of public AI tools have improved their privacy terms, and for some companies they're enough. The question is whether your contracts, regulators, and clients accept any outside processing at all. If they don't, private AI is the path.

When private AI makes sense

It's a strong fit if most of these are true:

• You handle data you're contractually or legally required to protect
• Your team does a lot of reading, writing, and searching through documents
• You have (or are willing to fund) a server or private cloud setup
• You want AI adopted company-wide, not just by a few people on their own accounts

If you're a small team with no sensitive data, a business plan from a public AI provider is usually simpler and cheaper.

How to start

1. List the work. Which tasks eat the most hours: searching for documents, writing reports, answering repeat questions?
2. List the limits. What data can never leave, and what do your contracts and regulators require?
3. Run a small pilot on one team and one set of documents, and measure the hours saved.
4. Then roll out, with sign-on, permissions, and training.

We set up private AI assistants on companies' own servers and private clouds: deployment, connection to your documents, access controls, training, and ongoing support. If public AI is off-limits for your business, tell us what you're working on.