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Corporate LLM: your own, GDPR-compliant private LLM

A Corporate LLM is a large language model (LLM) that a company runs under its own control: on its own or contractually bound infrastructure, connected to its own data, and governed by its own rules for access, storage and use.

Your employees already use AI, often with personal accounts and no rules. A Corporate LLM gives them a tool that writes and summarizes just as well, but where you decide where the data stays.

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What is a Corporate LLM?

“Corporate LLM” is a generic term, not a product name. It means a language model run for one specific company, instead of as an open service for anyone. Related terms are “private LLM” and “private AI”.

Three traits turn a language model into a Corporate LLM:

  • Control over the data: You decide where inputs and outputs are processed and stored.
  • Connection to company knowledge: The model answers questions from your documents and systems, usually through RAG.
  • Your own rules: Who's allowed to ask what, what gets logged, which content is off-limits.

The model itself doesn't need to be reinvented for this. Usually it's a capable existing model running in a controlled environment.

Corporate LLM vs. ChatGPT Enterprise: the difference

ChatGPT Enterprise is one of the business versions of ChatGPT. OpenAI commits to not using business data for training there and offers admin features for teams. For many companies, that's a reasonable step.

The difference is in the control:

Feature ChatGPT Enterprise Corporate LLM
Operator OpenAI You or a provider of your choice
Model OpenAI models freely selectable and interchangeable, including open models
Connection to company systems through the interfaces and connectors on offer built to your requirements
Dependency tied to one provider switch models without restarting the project

If you mainly want a good writing tool for many employees, ChatGPT Enterprise often serves you well. If you handle confidential data, want to choose your own models, or need to embed knowledge deep into your own workflows, you need a Corporate LLM.

GDPR & EU AI Act: full data sovereignty for your business

With a Corporate LLM, you decide which data the model sees, where it's processed and how long logs are retained. That's the foundation for justifying your AI use to data protection officers, works councils and customers.

The EU AI Act regulates AI systems by their risk level and takes effect in stages. Art. 4 of the EU AI Act has applied since February 2, 2025 to providers and deployers of AI systems, which includes companies that use AI. Since the Digital Omnibus of July 2026, it requires measures that promote your employees' AI literacy; it no longer prescribes a specific level of AI literacy. A Corporate LLM makes such measures easier: you know which model is in use, what it's used for and who has access.

  • Training: Your data is never used to train third-party models.
  • Contract: We sign a data processing agreement with you under Art. 28 GDPR.
  • Usage rules: AI policies that match the technology. On request, we develop them with you and train your employees.

RAG as the foundation: connecting company knowledge to your own LLM

A language model without access to your knowledge writes good text but knows neither your products nor your contracts. That's why RAG (Retrieval-Augmented Generation) is the first building block in almost every Corporate LLM: before every answer, the system searches your documents for the matching passages, and the model answers from them, with a source citation.

New documents are searchable immediately, access rights stay controllable per document, and the model itself can be swapped out without rebuilding the knowledge base. For how this works in detail, see RAG for Business.

Fine-tuning, AI policies and training

Alongside the Corporate LLM, we offer three additional services:

  • Fine-tuning: If the model needs to master a particular style or your industry language, we retrain it with your own examples. RAG remains the foundation for factual knowledge.
  • AI policies: We define with you which data may go into which tool and who uses AI for what. For a first draft, use our free AI policy template.
  • Employee training: Your teams learn to use the Corporate LLM safely and effectively. This is also a measure that supports AI literacy under Art. 4 of the EU AI Act.

Use cases: legal departments, financial services & companies with confidentiality obligations

A Corporate LLM pays off especially where data can't leave the building:

  • Legal and contract departments: summarizing contracts, comparing clauses, drafting documents based on your own templates
  • Insurers, banks and financial services: preparing cases, making policy terms and internal guidelines searchable
  • Manufacturers with engineering and project data: searching technical documentation, drafting quotes and reports, without handing know-how to external services
  • Companies with confidentiality obligations to customers: If you've contractually promised not to pass customer data to third parties, you need control over the AI processing

Day to day, employees use the Corporate LLM like a chat: for writing, summarizing, translating and asking questions against company knowledge. Through interfaces, it can also be built into automated workflows, see Process Automation.

How it runs: from pilot to production Corporate LLM

  1. Intro call (30 minutes by video, free): Which data, which users, which requirements?
  2. Define the architecture: hosting environment, model selection, connection to your login and permissions system. We document the decision so data protection and IT security can review it.
  3. Pilot: One department works with the Corporate LLM and a connected knowledge area.
  4. Evaluate: Usage, answer quality, employee feedback. What's missing, what's getting in the way?
  5. Rollout and support: More departments and data sources are added. We maintain the system, keep models current and adjust rules.
Frequently asked questions

Corporate LLM, answered briefly.

What is a Corporate LLM?

A Corporate LLM is a language model that a company runs under its own control. It runs on its own or contractually bound infrastructure, is connected to the company's data, and follows its own rules for access and storage. Employees use it like ChatGPT, but the data stays under the company's authority.

Which providers offer a GDPR-compliant Corporate LLM in Germany?

There are four groups of providers on the market: ready-made AI platforms that bundle several language models in one interface for employees; cloud platforms from large providers with servers in the EU, where your own IT department builds the solution; software that runs entirely on the company's own infrastructure; and service providers that build a Corporate LLM to order, connect it to company data and support it. titanspear.ai, an AI agency based in Langenselbold, Hesse, belongs to the fourth group: we select the right model with you, connect it to your company knowledge via RAG and sign a data processing agreement under Art. 28 GDPR. Your data is never used to train third-party models. Which group fits depends on whether data may leave your company, how many employees will use the system and how deeply it should be built into your workflows.

What does LLM mean in AI?

LLM stands for Large Language Model. It's an AI model trained on very large amounts of text, which is why it can understand and generate language. LLMs power applications such as ChatGPT, Claude or Gemini. They answer questions, summarize text, translate and write drafts. They only know company-specific knowledge if you provide it to them, for example through RAG.

What are LLM companies?

LLM companies are firms that develop and train large language models, for example OpenAI, Anthropic, Google or Meta. This is different from providers like titanspear.ai, which don't train their own foundation models but instead select, run and connect existing models to a company's data and workflows.

How can I train my own large language model (LLM)?

Training a language model from scratch requires enormous compute and data, and it's rarely worth it for a company. The usual path: pick an existing, open model, run it in a controlled environment, and connect it to your company knowledge through RAG. If the model also needs to master a particular style or industry jargon, fine-tuning with your own examples is added. We handle that too.

Is ChatGPT Enterprise GDPR-compliant?

ChatGPT Enterprise provides the contractual basis for GDPR-compliant use, but doesn't guarantee it. OpenAI offers a data processing agreement for it and commits to not using business data for training. Whether the use in your company is GDPR-compliant still depends on the individual case: which data employees enter, where it's processed and which internal rules apply. Your data protection officer should review that. If you process confidential data, want to choose the model yourself, or want to control access and storage by your own rules, a Corporate LLM is the better fit.

Is AI training mandatory for companies?

Not in the strict sense. Article 4 of the EU AI Act doesn't prescribe training. It has applied since February 2, 2025 to providers and deployers of AI systems, which includes companies that use AI, and requires measures on their staff's AI literacy. The Digital Omnibus softened this duty in July 2026: instead of ensuring, to their best extent, a sufficient level of AI literacy, companies now have to take measures that promote it. The regulation doesn't require fixed training hours or certificates; Germany's Federal Network Agency (Bundesnetzagentur) recommends documenting the measures. An obvious step is training on the system your employees actually work with, plus an AI policy that sets out which data may go into which tool. We offer both alongside the Corporate LLM.

Last updated: September 24, 2026

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