Eighteen months ago, the head of IT at a machinery manufacturer in Swabia rolled out a company-owned ChatGPT. 100% GDPR-compliant. Hardly anyone uses it. The engineering team uploads the 200-page requirements specification to Claude. Marketing has Gemini generate the trade-fair visuals. IT is currently testing Codex. All of it through private accounts with no data processing agreement (DPA) attached.
This is not an isolated case. In mid-sized companies, single-vendor AI always breeds shadow IT, because no single model is the best at every task.
That is exactly where Corporate LLM comes in: one central AI infrastructure for every department in the company.
Overview: GPT, Claude, Gemini, and Mistral are available as of today in one multi-model AI platform — with one login, one invoice, and one DPA. The EU-hosted models form the default selection, none of the providers may train on your data, and none of them caches your content. Four leading AI providers, one GDPR-compliant platform.
Why single-vendor solutions fail in mid-sized companies
The single-vendor AI platforms on the market force IT leaders into a decision nobody wants to make. Either a single provider — in which case the model is not the best choice for half the tasks, and employees route around it. Or an enterprise solution like Aleph Alpha with a six-figure minimum spend that an SME simply cannot shoulder.
Then there is the pace of the LLM market. Commit to a single provider today, and next quarter the best model will be sitting with a competitor. That now happens on a weekly cadence. Going single-vendor in this phase means vendor lock-in — and lock-in fuels exactly the shadow IT the single-vendor decision was supposed to prevent. A multi-model platform solves both problems: the best model for each task, one DPA, no lock-in.
GPT, Claude, Gemini, and Mistral in one multi-model AI platform
As of today, the models of all four leading AI providers are available in a single interface:
- OpenAI with GPT-5.5 as the EU flagship and GPT-5 mini as the budget-friendly option for reasoning, coding, and general-purpose tasks.
- Anthropic with Claude Opus 4.8, Sonnet 4.6, and Haiku 4.5 for agentic coding, legal drafting, long documents, and nuanced argumentation.
- Google with Gemini 3 Pro for multimodal and long-context work, Gemini 3 Flash and Flash-Lite as low-cost options, plus Nano Banana for image generation in the Atelier.
- Mistral with Small 3.1 as the cost-efficient all-rounder and Mistral OCR as a dedicated model that extracts content from PDFs, DOCX, and PPTX files, including tables.
Also available: open-source models such as DeepSeek V4 Pro, Kimi K2.6, and GLM-5.1 for teams that explicitly enable them.
In the chat, you switch models via the picker without losing your conversation history. In the Atelier, image generation runs on Nano Banana. In the Library, Mistral OCR extracts your documents in a way that preserves tables and diagrams.
Which AI model for which task?
| Task | Recommendation | Why |
|---|---|---|
| Analyzing long contracts or case files | Claude Opus 4.8 or Gemini 3 Pro | Both offer 1M-token context, EU-hosted, strong at long-document analysis |
| Writing and debugging code | Claude Opus 4.8 (top pick, EU-hosted); Sonnet 4.6 for price/performance | SWE-bench Verified: Opus 4.8 at 88.6% among the leading models, EU-compliant |
| Understanding or describing images | Claude Opus 4.8 or Gemini 3 Pro | Both multimodal with vision support, EU-hosted |
| Generating images | Nano Banana | Image generation in the chat Atelier, EU-hosted via Google Cloud |
| Extracting tables from PDFs | Mistral OCR | Dedicated layout model, EU-native, Markdown output for business documents |
| Fast routine answers | Gemini 3 Flash-Lite or Mistral Small 3.1 | The most affordable EU-hosted models, ideal for standard requests |
This table is built into the model picker as guidance, so even a new team member reaches for the right tool on day one.
Is the multi-model platform GDPR-compliant? Data protection by default
Three guarantees apply to each of the four model providers:
- EU hosting as the default. The EU-hosted models form the default selection, documented in the sub-provider list in the DPA. Non-EU models are flagged in the picker and must be explicitly enabled per team.
- No training on your data. Contractually excluded with OpenAI, Anthropic, Google, and Mistral. Explicitly negotiated in every contract — not just claimed in an FAQ.
- No persistence at the provider. Once the response is generated, your content is gone from the model provider. What remains is your conversation in Corporate LLM, under your control.
If you still want to activate a non-EU model — say, for a clearly scoped research case involving no personal data — you enable it deliberately, per team. The non-EU status is displayed permanently in the chat header so every conversation stays transparent.
How to switch between GPT, Claude, Gemini, and Mistral
- Click the model icon at the top left of the chat.
- Pick the right model from the list. The EU badge instantly shows the hosting location.
- Ask your question or upload a file. Switch models mid-conversation without losing context.
For teams: under "Models" in the settings, you define which AI models are enabled per seat. Sales gets only the cost-efficient Mistral, engineering gets full access. Cost-center logic without an extra tool.
What does multi-model cost? Availability and pricing in every plan
Multi-model is included in every paid plan. No surcharge for the feature. Token costs vary by model and provider, and you can track your running usage budget live in the dashboard. New models are added as soon as they support EU hosting — no action required on your side.
When multi-model is not the answer
Multi-model is not a universal fix. Three cases where a single-vendor setup is cheaper or simpler:
- Fewer than 15 seats with one clear use case. If the whole team writes Word documents and nothing else, GPT inside the office suite is enough. The picker overhead is wasted complexity here.
- Power users with a fixed model preference. Someone who has spent three years working exclusively with Claude in the console does not want a picker. Multi-model helps teams where beginners and experts share the same interface.
- Industries with air-gap requirements. Defense, the inner core of pharma, core banking systems: there, the model belongs in your own data center (see the self-hosted route), not in a multi-model cloud.
One thing multi-model does not change: if you want the best model for each task, you need onboarding so employees learn when to reach for which model. Without a picker guide, 8 out of 10 users stick with the default model, and the multi-provider advantage stays stuck in the backend.
Conclusion: four AI providers, one GDPR-compliant platform, one DPA
Four AI providers in the same chat. One DPA instead of four. One invoice instead of four. And your data protection officer finally gets an answer that isn't "we're working on it."
More background on the SME platform decision:
LLM platform for the mid-market: the 4 routes.
If you don't have access yet: create a free account. Existing customers simply open the chat — the model picker sits right in the header.
Frequently asked questions
Which AI models are included in Corporate LLM?
The active models from all four providers — OpenAI, Anthropic, Google, and Mistral. Specifically: GPT-5.5 and GPT-5 mini, Claude Opus 4.8, Sonnet 4.6, and Haiku 4.5, Gemini 3 Pro, Gemini 3 Flash and Flash-Lite, Nano Banana for image generation, Mistral Small 3.1 for routine tasks, and Mistral OCR for documents. On top of that, open-source models such as DeepSeek V4 Pro, Kimi K2.6, and GLM-5.1. New models are added continuously.
Is Corporate LLM GDPR-compliant with all four model providers?
Yes. The EU-hosted models form the default selection. There is a data processing agreement (DPA) with each of the four providers, training on your data is contractually excluded, and your content is not cached by the provider. Individual models without EU hosting (such as open-source models or special high-performance variants) are flagged in the picker and must be explicitly enabled per team. We disclose the sub-provider stack in the DPA.
Can we switch between models in the middle of a conversation?
Yes. In the chat header, you select a different model via the model picker, and your conversation history is preserved. So you can have Claude analyze a long document and hand the writing over to GPT in the same thread.
Which model for which task?
Long contracts: Claude Opus 4.8 or Gemini 3 Pro (each with 1M-token context, EU-hosted). Coding: Claude Opus 4.8 (top performance, EU-hosted) or Claude Sonnet 4.6 as the price/performance option. Understanding images: Claude Opus 4.8 or Gemini 3 Pro. Generating images: Nano Banana. Routine answers: Gemini 3 Flash-Lite or Mistral Small 3.1. Tables from PDFs: Mistral OCR. This mapping is built into the picker as guidance.
What if we want to stay strictly EU-only?
The EU-hosted models form the default selection in the picker. Non-EU models (such as open-source models or special high-performance variants) are active but must be explicitly enabled per team. The non-EU status is displayed permanently in the chat header so every conversation stays transparent.
