LLM Integrations: Language Models in Your Software and Processes.

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What is an LLM integration?

An LLM (large language model) is an AI model that understands and generates text. An LLM integration connects such a model to your own software, data, and processes, enabling it to perform specific tasks rather than simply providing isolated chat responses.

Consider an employee who receives a customer inquiry about a specific product. Without integration, they have to piece together the answer from manuals, old emails, and a colleague. With an LLM integration, they can ask the question directly in your own software and receive an answer based on your own documentation, complete with the source. Not generic text from the internet, but information that’s accurate within your organization. So this isn’t about a chatbot that provides isolated answers, but rather something that works seamlessly within your own systems, using your own knowledge.

Three or more? See what a language model can reveal.

When do you need an LLM integration?

The answer is somewhere in a document, email, or system, but no one remembers exactly where.

Answers

Your public chatbot provides answers that sound good, but it doesn't really understand your products and processes.

Reading

Someone is manually going through long texts, requests, or emails to extract the most important information.

Repetition

The same question keeps coming up, and the answer is buried in documentation that no one bothers to look up.

Waiting

All the knowledge is concentrated in one person, and as soon as that person is gone, work comes to a standstill.

Errors

Because the work is so boring and repetitive, a mistake slips through every now and then, and you have to go back and find it later.

Knowledge in one place

What we build, and whether it's right for you.

We build LLM integrations that run within your own software and take over or speed up your work. Not a standalone chatbot in a separate location, but functionality built into the systems your team already uses, powered by your own data. There are roughly three approaches, and they vary greatly in complexity and suitability. Whether to use a cloud-based or self-hosted solution is a separate consideration. Cloud models are often the practical choice: they’re available quickly and are cheaper to get started with. Self-hosted is the right fit when privacy or compliance requirements call for it. We build both and choose the approach on a project-by-project basis, not out of habit. That’s why we always start with your process, not the tool.

Curious? So are we

Which one fits your situation?

Three routes.

Direct API integration

The simplest form. Your software sends a request to a language model and uses the response. Good for well-defined tasks such as summarizing or classifying text. Quick to set up, provided you keep an eye on costs and what you include in the prompt.

RAG (retrieval-augmented generation)

The model generates responses based on your own documents rather than solely on its training data. It first identifies the relevant information and then formulates a response based on that. This is the approach for a knowledge assistant that must align with your internal knowledge, and it reduces the risk of generating inaccurate information.

Custom agent implementation

The model does not provide a single answer, but rather outlines a series of steps across multiple systems. It requires more power, but also more governance, because a model that acts autonomously must have clear boundaries.

There are four things that go wrong most often.

Rising costs. Iterative API calls without visibility into usage cause the bill to rise unnoticed. That’s why we build monitoring and rate limiting in from day one.
Prompt injection. In public interfaces, hidden instructions can end up in the input and cause the model to do something unintended. We never give a model broader access than its task requires.
Model drift. After a model update, its behavior may change, causing responses to differ from before. After every update, we verify that the model is still doing what it’s supposed to do.
Data leaks via prompts. Sensitive information can be inadvertently leaked through a prompt. We define in advance which data may and may not be shared, and build in reviews where necessary.

Curious about what a language model can do in your software? Outline your use case.

Jarno Rutjes - Business Director bij Cube - Oldenzaal

Jarno Business Director

How we set up an LLM integration.

Create

We start by asking what knowledge or process you want to unlock, and what a good solution looks like in your context. Together, we identify where the benefits lie and what data is needed to achieve them.

Unite

We choose the route that’s right for you: a direct connection, RAG, or an agent. In doing so, we weigh the pros and cons of cloud versus self-hosted solutions based on your privacy and compliance requirements, and determine what governance is needed.

Build

We integrate the model with your systems and use MCP to configure, on a per-role basis, what each user can view and do—regardless of which model you use. If you switch models later, your configuration will remain in place.

Evolve

We run the integration ourselves in production, monitor how the model behaves, and detect model drift when an update changes its behavior. Based on that, we make adjustments to ensure the answers remain accurate.

15+ years of experience: Cube as your digital partner.

Cube has been building mission-critical software for nearly fifteen years. For us, an LLM integration isn’t a one-off project, but a tool we add to systems that are already up and running—systems built by the very people who keep them in production. We choose the approach that best fits your use case, handle governance through MCP, and are transparent about the decision between cloud and self-hosted solutions.

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The tech we deploy for your success.

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OpenAI

OpenAI is an advanced AI technology that can conduct conversations, answer complex questions and make information understandable. It helps organisations make knowledge accessible to employees and customers, turn data into actionable insights and handle routine queries automatically.

Claude (Anthropic)

Claude (Anthropic) can process information and unlock knowledge without exposing or using sensitive data to train the AI model externally. This allows organisations to apply AI securely within internal tools, portals and business processes.

n8n

n8n is an open-source workflow automation platform that allows you to easily connect different applications and data sources. It allows you to automate tasks and processes, synchronise data and build workflows that fit exactly the way your organisation works.

Gemini

Gemini is an advanced AI platform that combines natural language and other data sources to automate and improve business processes. It can interpret information, perform analysis and perform tasks within different applications and workflows.

Laravel

Laravel, a leading PHP framework, accelerates development with tools like Eloquent ORM, Blade templating, and built-in security features. It provides an efficient and reliable foundation for building web applications.

Python

Python is known for its efficient performance and speed, making it ideal for data analysis, machine learning, and web development. With a wide range of modules and an active community, developers can create dynamic and customized solutions.

PHP

PHP, or Hypertext Preprocessor, serves as a powerful scripting language for creating dynamic web pages and applications. It allows you to develop robust and flexible online solutions tailored specifically to your needs.

Vue.js

Vue.js, an innovative JavaScript framework, enables you to create dynamic and interactive web applications. It offers not only speed and flexibility but also a modular architecture suitable for both simple and complex projects. Its excellent documentation and performance make it even more appealing.

LangChain

LangChain is a framework that enables language models to be linked to data sources, applications and workflows. It enables organisations to automate complex tasks, enrich knowledge bases and internal tools with AI, and make customer portals smarter.

We help corporations operate like startups, and startups grow into corporations.

Let's brainstorm

Want to integrate a language model into your own software?

Briefly describe the knowledge or process you want to make accessible. Rogier will work with you to determine whether an LLM integration is a good fit and what the smartest first step is.

Rogier Lohuis, Business Consultant bij Cube

Rogier Business Consultant

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Questions? No problem.

MCP is an open standard that enables AI models to communicate in a controlled way with external systems such as CRM, ERP, or internal databases. Originally developed by Anthropic, it is now governed by the Linux Foundation.

No. Cloud models are often the practical choice, but self-hosted solutions may be required if privacy requirements or governance mandates them. It’s a deliberate decision made on a project-by-project basis, not an automatic choice. Cube builds both.

RAG (retrieval-augmented generation) allows a language model to generate responses based on your own documents rather than just its training data. You need it when responses must align with internal knowledge—for example, a knowledge assistant based on your own documentation. It limits made-up answers and keeps information up to date.