I’ve been having the same conversation with enterprise clients for months now: “We want to use ChatGPT, but we can’t put our data into OpenAI’s systems.”

It makes sense. If you’re a law firm, you can’t send client communications to OpenAI. If you’re a healthcare company, you definitely can’t put patient information into ChatGPT. If you’re working on anything remotely sensitive, the current AI tools just aren’t an option.

But here’s the thing - the technology exists to solve this problem. We just need someone to build it properly.

The problem

Most people frame this as a “privacy vs. AI capability” tradeoff. Like you have to choose between protecting your data and getting the benefits of large language models.

That’s not actually the problem.

The problem is that all the good AI tools are designed for consumer use cases, where convenience matters more than data control. But enterprise needs are completely different - they need AI tools that are boring, predictable, and give them complete control over their data.

I spent last week talking to the IT team at a Fortune 500 company, and they laid out exactly what they need:

  • AI capabilities that match ChatGPT
  • Data that never leaves their environment
  • Audit trails for everything
  • Usage controls and compliance monitoring
  • The ability to fine-tune models on their specific use cases

None of this is technically difficult. It’s just not what anyone’s building right now.

What PrivateChatGPT would actually look like

So I’ve been sketching out what a proper enterprise AI platform would need to have:

Self-hosted deployment - The entire system runs in your environment. Your data never touches external servers. You can run it on-premises, in your private cloud, or in a VPC that you control completely.

Model flexibility - Support for multiple language models (not just OpenAI’s), including open-source models that you can modify and fine-tune for your specific needs.

Enterprise controls - User management, permission systems, audit logging, usage monitoring, and compliance reporting built in from day one.

API compatibility - Works with existing ChatGPT integrations so teams can switch over without rewriting everything.

Fine-tuning capabilities - Upload your own training data to create models that understand your industry, your company’s terminology, and your specific use cases.

The technical architecture isn’t that complicated. You’d have a model serving layer (probably using something like vLLM or TensorRT), a web interface that mimics ChatGPT’s UX, and enterprise management tools built around it.

Why this doesn’t exist yet

The big AI companies are focused on scale and consumer adoption. Building enterprise-focused tools is harder, more boring, and serves a smaller market.

But that smaller market is willing to pay serious money for this capability. I’ve talked to companies that would pay $100k+ annually for a properly built private AI platform.

And it’s not just about privacy. These companies want to fine-tune AI models on their proprietary data - customer service conversations, internal documents, industry-specific knowledge bases. That’s where AI becomes really powerful for enterprise use cases.

One law firm I know wants to train AI models on their case history and legal research. A consulting company wants models that understand their methodologies and can help with proposal writing. A pharmaceutical company wants AI that’s trained on their clinical data and regulatory submissions.

None of that is possible with public AI services, even if you weren’t worried about data privacy.

The business model that makes sense

This wouldn’t be a SaaS play. It would be more like enterprise software licensing - you pay for the platform, deploy it in your environment, and own the whole stack.

Core platform license - Annual license for the software platform, maybe $50-100k for a large enterprise depending on user count.

Professional services - Implementation, customization, and fine-tuning services. This is where you make the real money.

Support and updates - Ongoing model updates, security patches, and technical support.

Training and certification - Help enterprise teams learn to use these tools effectively.

The customers would be large enterprises with serious data sensitivity requirements: financial services, healthcare, legal, government, and any company dealing with proprietary research or competitive intelligence.

What’s stopping this from happening

I think it’s just that everyone’s focused on the consumer AI race right now. Building enterprise tools is less exciting than building the next ChatGPT killer.

But there’s a real opportunity here for someone who wants to focus on the boring-but-profitable enterprise market instead of trying to compete with OpenAI and Anthropic on consumer AI.

The technology exists. The market need is obvious. The willingness to pay is there. Someone just needs to build it properly.

The timeline that makes sense

This isn’t a 5-year moonshot project. Most of the components already exist as open-source tools. You could probably have a working prototype in 3-6 months and a production-ready platform in 12-18 months.

The real work is in the enterprise integration, security, and compliance features. But that’s exactly the kind of boring, important work that enterprise customers will pay premium prices for.

And the companies that get this capability first are going to have a significant competitive advantage. While their competitors are still debating whether they can use public AI tools, they’ll be fine-tuning AI models on their proprietary data and building AI-powered workflows that their competition can’t match.