Enabling an AI chatbot to access your company data offers numerous advantages, including faster information access, automated tasks, and improved team productivity.
To ensure successful implementation, it is important to address two key challenges:
- Data sensitivity: Determine whether your company’s data contains sensitive information that requires special protection.
- Data structure: Analyse how the relevant information is organised and structured within your data.
Assessing the sensitivity of your data will help determine the potential risks in the event of a leak of sensitive information, as well as the implications for protecting your intellectual property and the risk of exposure to attacks.
On the other hand, analysing the structural characteristics of your data will guide you in choosing a data model and a technique for integrating data into an LLM, such as RAG or fine-tuning.
By combining these assessments, you will be able to decide whether using an open-source or commercial LLM is better suited to your needs.
What are the differences between commercial LLMs and open-source LLMs?
- Commercial LLMs are large language models, such as ChatGPT (OpenAI) or Gemini (Google), maintained and provided by specialised companies. They offer a turnkey solution.
This first option is available only in the cloud and requires outbound data flow to external servers, exposing your company to data leakage risks.
The trade-off of a turnkey solution is a limited ability to modify options and model parameters. It is therefore important to find out about your access to model settings before committing to a provider — for example, for implementing fine-tuning if you wish to do so.
Particular attention must also be paid to usage costs set by the provider, including potential long-term fees that may be higher than those of local solutions.
- Open-source LLMs are, as their name suggests, open to all. Unlike proprietary commercial LLMs, open-source LLMs (e.g., Mistral, Meta’s Llama4) can be installed on a local machine or in a private cloud.
In general, open-source LLMs are less voluminous but also slightly less performant than commercial LLMs, although this gap tends to narrow over time (source: llm-stats.com).
Unlike a turnkey solution, IT architecture sizing is required to ensure sufficient computing power and a network infrastructure adapted to the use case. Cloud hosting can reduce this cognitive load, though it is still necessary to keep orders of magnitude in mind.
The open-source principle, which provides complete access to models, enables high levels of customisation and configuration, including the ability to fine-tune the model to specific needs.
The absence of per-query fees means costs are primarily driven by electricity and initial infrastructure. You are not tied to a provider, so you can switch models to keep pace with emerging ones.
After studying this topic, you will therefore be able to determine which type of LLM you need.
You can then consider the type of technologies (RAG and/or fine-tuning) used to integrate your company data.
What is RAG?
RAG (Retrieval-Augmented Generation) is an innovative approach that enables you to integrate your company data into a chatbot effectively. It allows you to provide more accurate, contextualised, and relevant responses by enriching the LLM’s capabilities with information from your own knowledge base.
The principle is simple: a structured database is created containing the key information needed to respond to user queries. This database is then connected to the LLM. Each time a query is submitted, the RAG system searches the database for information that is semantically closest to the query. This contextual information is then transmitted to the LLM, which uses it to generate a more accurate and relevant response.
The RAG’s “special” database is a structured knowledge base designed to quickly and efficiently extract the most relevant information from your data with each query.
Key advantages of RAG:
- Modularity: thanks to RAG’s modular architecture, you can update or replace your knowledge base or change LLM models without major modifications to the overall system.
- Flexibility: This method applies to both open-source and commercial models.
- Transparency: each LLM response can be easily verified by cross-referencing the documents extracted from your knowledge base. This transparency reinforces confidence in the reliability of the responses provided.
What is fine-tuning?
Fine-tuning is a powerful technique that enables you to customise an LLM to excel at specific tasks or meet particular needs.
The approach is to modify the model’s internal parameters to improve performance for your specific use cases. Fine-tuning means retraining an existing LLM on your company’s data.
Key advantage of fine-tuning:
- Uniqueness: unlike RAG, fine-tuning does not require maintaining a knowledge base — the LLM learns all your data during retraining.
Weaknesses of fine-tuning:
- Updates: fine-tuned model changes are not immediate and require retraining.
- Accessibility: fine-tuning is generally only available on open-source models.
What methodology does EURODECISION use to set up a customised CHATBOT?
What methodology does EURODECISION use to set up a customised CHATBOT?
At EURODECISION, we are experts in Data Science and Artificial Intelligence, and we understand the challenges involved in integrating your company data into AI chatbots. Our mission is to support you in a complete and personalised manner, from design through to maintenance of these solutions.
To develop the solution best suited to your needs, our approach begins with a scoping phase and in-depth analysis of your data and business requirements. We assess in detail the sensitivity of the information they contain and how they are organised.
We then advise you on choosing the LLM best suited to your needs: either a commercial or an open-source model. We also advise you on the type of hosting — local or cloud. We take into account your technical and budgetary constraints to guide you. We also ensure precise sizing of your IT infrastructure by evaluating computing power, storage, and network requirements to guarantee optimal performance.
Following this scoping phase, the development phase can begin, with the concrete implementation of your solution. Whether through a RAG architecture or fine-tuning, we develop bespoke solutions using agile project management to deliver deployable, functional tools throughout the development process.
Our commitment does not stop at one-time implementation of the solution. We can provide ongoing technology monitoring to track the latest advances in AI and advise you on optimisation and update opportunities. Finally, to ensure your team’s autonomy, we offer knowledge transfer and skills development to help you understand and maintain your solutions over time.
With EURODECISION, you benefit from expert support to fully exploit AI’s potential and transform your business.