LLM integration for enterprise: architecture and best practices

In short: A dependable enterprise LLM integration treats the language model like any other external service: behind your own interface, with validated inputs and outputs, logging and clear access rights. Company knowledge usually reaches the model through retrieval (RAG), and fine-tuning is rarely the first step. You secure quality with a fixed set of test cases and data protection with EU processing, a data processing agreement and data minimisation.

The model as a replaceable component

The most common architectural mistake is scattering direct calls to one provider across the whole codebase. Models move fast, prices change, and sometimes data protection requires a different provider or a self-hosted model.

So build your own intermediate layer, often called an LLM gateway. It wraps the provider APIs, manages keys, enforces limits per user or department and logs every call. Business logic only talks to this layer, which means switching models touches one place instead of a hundred.

Structured outputs instead of free text

As soon as a model's answer is processed further, for example in a CRM or ERP, you need a fixed structure. The major providers support structured outputs that follow a JSON schema, or tool calls with defined parameters. Validate the result in your own code anyway before it reaches your systems.

Connecting company knowledge: RAG or fine-tuning

A language model does not know your products, contracts or internal processes. There are two ways to change that.

With retrieval-augmented generation (RAG), you look up the relevant documents for each request and pass them to the model as context. Documents are split into chunks and stored as vectors in a database, for example PostgreSQL with the pgvector extension. RAG has practical advantages: content is current immediately, sources can be shown, and access rights can be checked per document.

Fine-tuning adapts a model with your own examples. It is better suited to a particular style or a fixed output format than to factual knowledge. Most enterprise projects get further with good prompting and RAG. Our post on AI-powered data extraction shows how to read data reliably from documents.

Do not forget access rights

An internal assistant may only show an employee documents she could see anyway. Filter by the user's permissions at retrieval time. An instruction in the prompt such as "do not show confidential data" is not access control.

Security and data protection

Prompt injection

Text the model processes can contain instructions: an email, a web page, an uploaded PDF. The model does not reliably distinguish between your instructions and those inside the content. Limit what the model is allowed to trigger. Read actions are less critical than write actions, and anything that costs money, deletes data or communicates externally should require human confirmation.

GDPR and the EU AI Act

Find out where data is processed, whether the provider uses it for training and whether a data processing agreement is in place. Many providers offer processing in EU data centres and exclude training on customer data in their API terms. Send only the data the task needs. Depending on the use case, obligations under the EU AI Act apply as well, such as transparency duties. Our article on the EU AI Act and your AI projects gives an overview.

Quality, cost and operations

Evaluation with fixed test cases

Language models are not deterministic, and a small prompt change can make things worse somewhere unexpected. Build a collection of real example requests with expected results and run it on every change to prompt, model or retrieval. With structured outputs many checks can be automated. For free text, scoring by a second model helps, backed by human spot checks.

Cost and latency

Cost depends on the number of tokens processed. Smaller models are enough for classification and extraction, and larger ones should be used only where needed. Provider-side prompt caching cuts the cost of long, repeated contexts. Streaming shortens perceived waiting time in chat interfaces. We show how this comes together in a production chatbot in building custom AI chatbots from GPT to production.

Frequently asked questions (FAQ)

Do we need our own self-hosted model?

Only in specific cases, such as highly sensitive data or strict rules that nothing may leave your own data centre. Open models can be self-hosted, but they need GPU infrastructure and maintenance. For most companies, a major provider with EU processing is the more pragmatic start.

How do we stop the model from making things up?

You cannot rule it out completely. You lower the risk considerably if the model answers only from supplied sources, shows those sources and is explicitly allowed to say "I don't know" when information is missing. A human reviews critical results.

Where is the best place to start?

With an internal use case that has a clear benefit and limited risk, for example summarising support requests or searching internal documents. There you learn the architecture, costs and limits before customers see the system.

Conclusion

A successful LLM project depends less on the chosen model than on the architecture around it: a replaceable connection, proper permissions, validated outputs and measurable quality. Lay these foundations early and you can switch models later without a major rebuild. If you want to integrate language models into your software or workflows, talk to us about AI integration and automation.