Develop AI models specifically trained on your data and optimized for your unique use cases.
Models optimized for your specific use cases with quality results
You fully own the developed model and your data remains confidential
Reduce your dependency on external APIs and control your costs
Models trained on your vocabulary, style and processes
Solutions that adapt to your business and data growth
Continuous model improvement with new data and feedback
Evaluating the quality and quantity of your data, identifying business needs and defining performance objectives.
Cleaning, annotation, enrichment and structuring of your training data to maximize model quality.
Selecting the optimal base model, supervised training, hyperparameter optimization and cross-validation.
Secure production deployment, integration into your systems, performance monitoring and continuous improvement.
Adapting GPT, Claude, Llama or Mistral to your domain with your own data for ultra-relevant responses
Specialized models to categorize your documents, emails, support tickets or content according to your criteria
AI trained to automatically identify and extract key entities and data from your texts
Custom algorithms to anticipate your KPIs, churn, demand or specific risks
Vectorization optimized for your domain enabling ultra-precise semantic search
Intelligent suggestion systems based on user behavior and preferences
Creative AI adapted to your tone, style and guidelines to produce quality content
Models trained on your normal data to automatically identify deviations and incidents
Fine-tuning involves specializing a pre-trained AI model on your business data. Instead of using a generalist model, you get a model that's an expert in your domain — more accurate, faster, and more cost-effective to run.
Fine-tuning is the technique that adapts a language model (LLM) to your specific domain. A generalist model like GPT knows a little about everything, but a fine-tuned model trained on your data becomes an expert in your field. It understands your technical vocabulary, your processes, and your quality standards. For example, a model fine-tuned for a law firm will understand the subtleties of legal language better than a generalist model. A model fine-tuned for a manufacturer will recognize production defects specific to its manufacturing line. This specialization is what separates a novelty from a high-performing business tool.
Prompt engineering involves crafting precise instructions to guide an existing model — it's quick to implement but limited for complex cases. RAG (Retrieval Augmented Generation) enriches model responses with your enterprise documents — ideal for knowledge bases. Fine-tuning modifies the fundamental behavior of the model — essential when you need a specific style, format, or domain expertise. Often, the best solution combines all three: a fine-tuned model for your domain, enriched by RAG with your current data, and guided by optimized prompts.
Fine-tuning begins with collecting and preparing your training data: question-answer examples, annotated documents, historical conversations. We clean and structure this data to maximize learning. Then we select the most suitable base model (open source or proprietary), define training hyperparameters, and run the fine-tuning. After training, we rigorously evaluate the model on a test set representative of your real-world cases. The process iterates until the required performance level is achieved. Typical timeline: 2 to 6 weeks depending on complexity.
Unlike SaaS solutions where your data feeds a shared model, a fine-tuned model belongs to you. You can host it on your own infrastructure (on-premise or private cloud) with full control over your data. This independence eliminates vendor lock-in and guarantees the confidentiality of your intellectual property. The inference cost of a fine-tuned model is also significantly lower than that of generalist model APIs, because the model is optimized for your specific use case.
Discover key concepts related to this solution
It depends on the type of model. For LLM fine-tuning, a few hundred examples may suffice. For classification or prediction models, we generally recommend at least 1000-5000 labeled examples. We evaluate your data during the initial audit.
A complete fine-tuning project typically takes 6 to 16 weeks: data audit (1-2 weeks), dataset preparation (2-4 weeks), training and optimization (2-6 weeks), testing and deployment (1-4 weeks). We often start with a 4-week POC.
Absolutely. We can deploy the model on-premise on your servers, in your private cloud, or on whatever infrastructure you choose. You maintain full control over hosting and security.
Your training data remains strictly confidential and is never shared. We sign NDAs, apply encryption, and can work in your secure environment. The final model belongs 100% to you.
Fine-tuning is a machine learning technique that takes a pre-trained AI model (like GPT, Llama, or Mistral) and specializes it on your business data. The model retains its general capabilities while becoming an expert in your domain. Think of it as training an already qualified employee on your company's specific operations.
It depends on your needs. RAG is preferable when your data changes frequently (documentation, product catalog) — the model retrieves your data in real time. Fine-tuning is preferable when you need specific behavior (response style, format, domain expertise). Often, combining both delivers the best results.
The volume depends on the use case. For style or format fine-tuning, a few hundred quality examples are sufficient. For deep domain specialization, several thousand examples are preferable. Quality always trumps quantity — 500 well-structured examples are worth more than 10,000 noisy ones. We guide you through data preparation.
Open source models (Llama, Mistral, etc.) offer more control, no recurring API costs, and total independence. Proprietary models (GPT, Claude) are often more performant out of the box but create vendor dependency. Our recommendation: open source for on-premise deployments and high volumes, proprietary for rapid prototyping and cases requiring the highest absolute performance.
We evaluate each model on a representative test set using objective metrics: precision, recall, F1-score, and human evaluations of response quality. We systematically compare against the non-fine-tuned base model to quantify the improvement. Typical results show 20-50% improvement on domain-specific tasks.
Let's discuss your specific needs and create together a perfectly adapted AI solution.