AI model integration
Generative AI is no longer an experiment: it is a competitive advantage. We integrate LLMs, RAG and intelligent automation into your systems, with your data and under your control.
AI that works with your data
A generic chatbot adds little value. We build assistants based on RAG (Retrieval Augmented Generation) that answer from your documentation, your databases and your real processes, with citations and source traceability. Your data stays private: we deploy on your infrastructure or in the cloud with the security controls your business demands. And we comply with the European AI Act: we classify the risk of each use case and document the system as the regulation requires.
From proof of concept to production
Many AI projects die in the data scientist notebook. We integrate AI into your daily operations: process automation with classification and information extraction, API integration with your existing applications, continuous evaluation of response quality and inference cost monitoring. AI that works in production, not just in the demo. We start with one concrete, measurable use case: automating customer support, extracting data from documents, classifying requests. Quick wins that finance the next phase.
What we can integrate
- Chatbots and assistants powered by LLMs
- RAG over your private data with citations and traceability
- Intelligent automation of document processes
- API integration with your current applications
- Private or cloud deployment with security controls
- Continuous quality evaluation and monitoring
AI integration for companies: from idea to deployment
Generative AI is no longer just for big tech: SMEs and departments of every kind already use it to serve customers, process documents and automate repetitive tasks. We are developers specialised in integrating AI models (LLMs, RAG, computer vision) into existing business systems. And we do it with guarantees: EU AI Act compliance, data privacy and continuous quality evaluation.
How we integrate AI into your business
A pragmatic approach: start with one concrete use case, measure results and scale.
Use case identification
Together we find the point where AI creates the most value with the least risk: customer support, document processing, information classification. We define measurable goals before writing a single line of code.
Proof of concept
We build a working prototype with real data in a few weeks. You test it with real users and we measure: accuracy, response quality and cost per use. Only what works goes to production.
Integration and deployment
We connect the model to your applications via APIs, deploy on your infrastructure or in the cloud with the right security controls, and document the system to comply with the European AI Act.
Continuous improvement
We monitor quality, inference costs and user satisfaction. We retrain and adjust with real data so the system improves month after month instead of degrading.
Frequently asked questions about AI integration
What is RAG and what is it for?
RAG (Retrieval Augmented Generation) is the technique that lets an AI model answer using your private data: documents, databases and manuals. The system retrieves the relevant information and generates the answer with citations, minimising errors and hallucinations.
Is my data safe when using AI?
Yes, if the system is designed properly. We deploy models on your infrastructure or on clouds with contractual guarantees, encrypt data and define which information each system may process. Your data is never used to train third-party models.
What about the European AI Act?
The European AI Act requires AI systems to be documented and classified by risk level. Every project of ours includes compliance analysis from the design stage: system classification, technical documentation and transparency and human-oversight measures where required.
Where do we start if we have never used AI?
With one concrete, measurable, low-risk use case: for example, an assistant that answers questions about your internal documentation or automatic data extraction from invoices. In a few weeks you have a proof of concept with real metrics to decide whether to scale.