LLMs & AI Agents · Model adaptation
Custom LLMs: Why Tailored Language Models Matter for Businesses
Answering customer queries, summarising emails, structuring information: large language models can take on a lot of routine work. How well they do it depends on how closely they are tuned to a company’s tasks, data and systems.
Aleksandra Osztynowicz
Key takeaways
- Standard LLMs are generalists. For specialised business tasks they have to be adapted.
- There are three routes: prompt engineering for clearly defined tasks, RAG for knowledge with sources, fine-tuning for very specialised vocabulary or high volumes.
- Most projects do not need a model trained from scratch. The leanest approach that meets the requirement is the sensible choice.
- LLMs run via privacy-compliant APIs, in the cloud or on-premise on your own servers.
What is an LLM?
A large language model (LLM) is an AI model trained on very large amounts of text that can understand and generate natural language. It analyses text, summarises content and answers questions. Standard models are built for a broad range of tasks. That makes them versatile, but often too imprecise for specialised business tasks: they know neither a company’s internal documents nor its terminology or rules.
Selecting and integrating the right model is therefore a challenge for many companies. They often lack the technical expertise to find the right approach and integrate the model into existing systems.
Three ways to adapt an LLM
Training a model from scratch is rarely necessary for companies. Usually a pre-trained base model is adapted in one of three ways, starting with the leanest one that meets the requirement:
| Approach | Principle | Fits when | Effort |
|---|---|---|---|
| Prompt engineering | A general model with prompts and output formats tailored to the task. | the task is clearly defined: extraction, classification, summaries. | low |
| RAG | The model answers from documents retrieved at the moment of the question and cites the source. | knowledge changes often and every answer needs a source. | medium |
| Fine-tuning or own model | A base model is trained further on examples from your own data. | vocabulary is very specialised, the output style is fixed or a smaller model must handle high volumes. | high |
The approaches can be combined: a fine-tuned model can work behind a retrieval step. Every approach also needs validation, meaning good examples and a test dataset against which results can be measured. With fine-tuning, the quality and breadth of the training data determine how well the model grasps terminology, complex questions and the context of a task.
Examples from our projects
Prompt engineering: Fr. Meyer’s Sohn
The logistics company receives emails every day, no two of them alike. Our solution extracts delivery dates, quantities and other client-defined details from German and English emails and delivers them in structured form for further processing. It uses GPT models with prompts tuned to logistics emails, runs on the client’s own servers and cut manual extraction effort by around 80%.
RAG: apoQlar
For the MedTech company, an assistant answers questions from hundreds of product documents in seconds, and every answer names the document it comes from.
Custom models: arvato
For the call centres of arvato, part of the Bertelsmann group, we trained custom models for Polish language processing. Call summaries are now produced fully automatically, in one uniform format for all teams.
Where tailored LLMs help
- Customer service: answering simple queries automatically or providing first support before a person takes over.
- Information extraction: delivering relevant details from emails and documents in structured form, for example in logistics.
- Summaries: condensing extensive documents, such as policy texts as soon as they are published.
- Internal assistants: answering questions on product and reference documents with sources.
The EU policy summaries case study shows how policy documents are summarised automatically.
The right environment for operation
Besides the model and adaptation, where the LLM runs matters. Privacy-compliant APIs are quick and flexible to deploy. With sensitive data, running in the cloud or on-premise can make sense, with all data processed on your own servers. Which option fits depends on data protection requirements and IT infrastructure, and it is best agreed in writing before anything is built.
Why experts pay off in LLM projects
A tailored LLM requires technical expertise and an understanding of business requirements: from the right approach through data and validation to integration into existing infrastructure. Experienced teams make sure the model delivers the intended value and can be maintained and adapted to new requirements later. More on our approach on the LLM development page.
Which approach fits your task?
In the process analysis we work out whether prompt engineering, RAG or fine-tuning carries your use case, which data is needed and where the model should run.
Request a process analysisFrequently asked questions
A language model tuned to a company’s tasks, data and terminology, through prompt engineering, retrieval-augmented generation, fine-tuning or a combination.
Usually not. Many tasks can be solved with prompt engineering or RAG. Fine-tuning or custom models pay off for very specialised vocabulary, a fixed output style or high volumes.
Yes, via privacy-compliant APIs or by running in the cloud or on-premise, where all data is processed on your own servers.
A proof of concept is often ready within a few weeks. Full implementation depends on complexity, data and integration depth.
At theBlue.ai, the process analysis has a fixed price from €3k. Implementation typically runs between €25k and €100k, with a scoped estimate at the end of the analysis.
Aleksandra has been building custom AI solutions at theBlue.ai since 2021, with a focus on agentic implementations and local LLM deployments that precisely fit enterprise needs. As an AI Engineer, she helps organizations automate their processes with production-grade systems, from on-premise open-source models to RAG-based internal knowledge bases for regulated industries, and continuously expands her knowledge in the rapidly changing world of artificial intelligence.
In her articles, she shares practical experience from real enterprise AI projects and shows that deploying AI in companies doesn’t need to be complicated.