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LLM agencies in Cologne compared
Connecting a language model takes an afternoon. Making it reliable, secure, and affordable inside a business process is the actual work. These agencies integrate LLMs like Claude, GPT, Gemini, or open-weight models into existing applications, distinguishable by model, framework, hosting, and verified proof.
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Guide
What "LLM integration" actually covers
At its core, this kind of agency connects language models to your data and systems: assistants that answer from internal documents, extracting structured data from emails and PDFs, summarization, classification, translation, text generation to spec. That involves more than just calling a model: prompt design, connecting retrieval (RAG for short), evaluation, protection against faulty output, cost control, and ongoing operations. The line to the AI agents category is blurry: once the model itself starts triggering actions in your systems instead of just answering, it's more accurately called an agent.
How to recognize a good agency
The model choice should be justified, not assumed. Claude, GPT, Gemini, Mistral, Llama: each of these models has its own strengths, prices, and data protection terms, and a good agency tests two or three candidates on your own data before committing. Profiles let you filter by Claude, OpenAI, or Mistral.
The stack used (LangChain, LlamaIndex, the Vercel AI SDK, pgvector or Qdrant as a vector store) largely determines how maintainable the solution stays later. Feel free to ask why this particular stack and not another.
Without evaluation, quality can't be measured, only claimed. A test set of real queries with target answers belongs in every serious proposal, along with metrics on hit rate, hallucination rate, response time, and cost per query.
The data flow deserves a close look: which data goes to which provider, where is it stored, what gets logged? EU hosting and a data processing agreement are mandatory here, not optional extras.
And the real work only starts after go-live: model versions get deprecated, prices change, prompts drift over time. Clarify monitoring, cost alerts, and update processes before the system goes live.
Costs
Agencies typically charge €120 to €200 per hour. A proof of concept on a clearly scoped use case costs €8,000 to €25,000 and takes three to six weeks. A production assistant connected to your documents, with access control, evaluation, and operations, runs €30,000 to €120,000. On top come ongoing model costs, ranging from under €100 a month at low volume to several thousand euros for thousands of daily queries. A reputable agency works this out based on your expected volume before the project starts and builds in cost limits from the outset.
What LLM integration is used for
A knowledge assistant for staff covering manuals, contracts, and tickets. Automated capture of incoming invoices and orders from emails. First drafts of quotes, RFP responses, or product copy to spec. Classifying and routing customer inquiries. Translating and summarizing large document collections. An API through which your own software offers language features.
Score, search, contact
The ranking follows the score, built from ratings, verified proof, verification, completeness, and activity; paid placements are clearly marked. In the search tool, combine model, framework, EU hosting, and budget. Or submit a request with your specific use case.
Frequently asked questions
LLM agencies: questions and answers
What does integrating a language model cost?
A proof of concept runs €8,000 to €25,000, a production assistant with data connection, evaluation, and operations €30,000 to €120,000. On top come ongoing model costs from under €100 to several thousand euros a month, depending on query volume. Agencies charge €120 to €200 per hour for this work.
Can I connect my own company data to a language model?
Yes, through retrieval-augmented generation. Your documents go into a vector database, and the model receives the matching excerpts as context for each question. Nothing gets trained this way, and with EU hosting, the data never leaves the EU. Specialized providers for such RAG systems can be found in their own category.
How do you prevent hallucinations?
You can't prevent them entirely, but you can manage them well: through retrieval with source citations, tightly scoped prompts, validation of structured output, follow-up questions when uncertain, and evaluation with real test cases instead of gut feeling. Have the agency give you a measured error rate, not just a promise.
Claude, GPT, or an open-source model?
Claude and GPT lead on quality for complex language tasks. Open-weight models like Llama or Mistral, on the other hand, can run on your own infrastructure when data can't leave the building at all. At high volumes with simple tasks, smaller models are usually the more economical choice. In the end, this decision should follow from an evaluation on your own data, not the agency's general preference.
How long does an LLM integration take in practice?
A proof of concept is done in three to six weeks, a production assistant needs two to four months including evaluation, access integration, and setting up operations. More decisive than the model itself is usually how accessible and clean your data already is.