Find out how to enhance your business assets with a Data Strategy and Roadmap that helps you exploit the advantages of LLM models and Generative Artificial Intelligence.
Without a robust and properly configured Data Infrastructure, fully unlocking the transformative potential of Generative AI in your Company could become a complex challenge.
Discover the key elements of a winning data strategy, focused on the development and effective management of Generative Artificial Intelligence projects in your Company.
Immerse yourself in the epochal change that is redefining every sector. Discover how efficient data processing is becoming the cornerstone for advanced applications utilizing large language models, Generative AI, and Semantic Search.
Understand the power of vector data, an essential information representation that enables machines to comprehend and retain information long-term. Learn to manage the complexity of attributes and features generated by AI models, such as Large Language Models (LLMs).
You won't have to pay anything and you can also extend it to your colleagues and collaborators.
60/120 minutes in which we define, vote and prioritize use cases suited to your specific business context.
Applications and practical advice to try in your business.
Receive a ROADMAP for the implementation of Generative AI in the Company.
Digital transformation, fueled by the exponential advancement of Artificial Intelligence (AI), is redefining the competitive foundations of virtually every sector. This revolutionary movement, centered on AI, underscores the critical importance of efficient and strategic data processing as the foundation for developing advanced applications. In this context, Large Language Models (LLMs), Generative AI, and Semantic Search emerge as catalysts for innovation, pushing companies towards new frontiers of automation, personalization, and data understanding.
The ability to manage massive volumes of both structured and unstructured data becomes an indispensable requirement to fully leverage the potential of AI. The quality, relevance, and accessibility of data determine not only the effectiveness of generative AI models but also the speed at which they can be implemented and scaled within business environments. Consequently, efficient data processing—through techniques such as cleaning, standardization, and enrichment—becomes the cornerstone for building robust and adaptive AI solutions.
Moreover, the continuous evolution of language models and AI algorithms necessitates a dynamic data environment capable of supporting rapid iterations and agile experimentation. This implies a transition from traditional data management to more flexible and scalable infrastructures, able to facilitate the integration and analysis of increasingly complex data sets.
The effectiveness and speed in developing Generative Artificial Intelligence applications crucially depend on the quality of the data environment on which these technologies are built and implemented. A carefully curated knowledge base, coupled with cutting-edge data architecture, forms the foundation on which generative AI applications can be successfully developed, tested, and scaled. This approach not only shortens development and innovation cycles but also ensures that solutions are robust, reliable, and capable of adapting to the evolving needs of the company and its users.
During this segment of the workshop, we will delve into how the careful and strategic management of the knowledge base can directly influence the performance and accuracy of generative AI applications. We will discuss methodologies for data enrichment, semantic annotation, and data quality verification, which are essential elements for feeding AI models with precise and relevant information.
Through a series of practical examples and best practices, participants will gain comprehensive insights on how to structure a data ecosystem that facilitates the efficient development and implementation of generative AI solutions. We will discuss how optimizing the data environment not only enhances the effectiveness of AI applications but also accelerates time-to-market and improves the end-user experience.