Artificial Intelligence

We develop proof of concepts to explore the potential and viability of all these technologies in different application areas.

• Generative AI: use of advanced models for text, images, voice, code, and other types of digital content generation.
• Language Models (LLM and SLM): working with Large Language Models (LLM) and Small Language Models (SLM) to adapt AI to different use cases, optimizing efficiency and response capacity.
• Multimodal Models: development and application of models that combine text, image, audio, and other sources of information to offer richer and more contextualized solutions.
• Fine-tuning of language and computer vision models: adapting pre-trained models to specific use cases to improve their accuracy and relevance.
• Natural Language Processing (NLP): text analysis and generation, machine translation, automatic summarization, sentiment analysis, and virtual assistants.
• RAG (Retrieval-Augmented Generation): combination of information retrieval and text generation to improve the accuracy and reliability of AI-generated responses.
  Information extraction from complex documents: use of advanced models to interpret, structure, and analyze information from large and technical documents.
• Computer Vision: image and video recognition, object detection, image synthesis, and scenario enhancement using generative AI.
• Prediction Models and Advanced Analytics: applying machine learning and deep learning algorithms for trend prediction, anomaly detection, and service personalization.
• Process optimization and automation: using AI to improve efficiency in areas such as logistics, resource planning, or automated decision-making.
  Ethics and explainability in AI: developing transparent and responsible models, ensuring interpretability and reliability of intelligent systems.