We can develop a tailored content generator for your organisation that provides a strong user experience, delivers high‑quality output, and ensures that your data remains private.
Many people already use ChatGPT or similar tools to ask questions and receive answers. However, many organisations want to ensure that their data stays private and is not used by OpenAI or other providers to train models.
In addition, it often makes sense to wrap this basic chat functionality in a dedicated user interface (UI) that makes the process more user‑friendly and results in higher‑quality output.
A simple question‑and‑answer interaction with ChatGPT can be very effective. But by implementing a custom UI on top of an LLM, you gain control over both input and output — leading to a significantly better user experience and better results.
The technologies we use in our content generator make it easy to extend and tailor the solution specifically to your organisation.
The core concept behind RAG is simple, and we can quickly set up a proof of concept (PoC) that demonstrates the power of the technique.
In short, RAG is a method that allows you to use a pre‑trained LLM — such as ChatGPT — while enabling it to answer questions based on your own data. An LLM enriched with your organisation’s datasets ensures responses that are both relevant and up to date for your business. The next step is to ensure that quality, monitoring, and security meet your organisation’s requirements.
RAG works by taking a query (for example, a user question), searching indexed data for relevant information, and then using that information together with the original question to generate a response.
This not only improves accuracy, but also ensures that responses are adapted to the specific context — leading to better decision‑making based on the most relevant internal and external sources.
We use function calling as a powerful technique in our implementation of LLM‑based applications.
Function calling acts as a bridge between LLMs and other systems or data sources, and can be seen as a way of extending the AI system’s capabilities by allowing it to interact with other tools or databases.
You can think of function calling as a colleague who not only understands and responds to your requests, but can also reach out to other colleagues or resources to retrieve information or perform tasks.
This interaction is governed by a set of instructions that the AI follows to achieve the desired outcome — similar to asking a colleague to retrieve a file or update a record.
As a result, function calling can be used to trigger actions in other systems, such as updating a database, sending notifications, or publishing content online.
It is not a question of whether your digital solutions will use AI — but when, how, and for what purpose.
Read more about our AI services and how you can unlock the potential of AI here:
