The real potential of Artificial Intelligence (AI) lies in its ability to integrate into companies internal processes, optimizing operational efficiency, personalizing services, improving strategic decision making and enhancing innovation, ultimately leading to a sustainable competitive advantage.
In addition to traditional digital project development methodologies, such as agile methodologies, artificial intelligence (AI) projects present enough differences to justify the application of specialized methodologies.
For Machine Learning and Deep Learning projects, where large amounts of data are handled and advanced algorithms are applied to find and predict patterns, it is recommended to use CRISP-DM (Cross Industry Standard Process for Data Mining).
On the other hand, projects that integrate generative AI and natural language processing (NLP) into business processes require a specific methodology.
The Composite AI approach consists of the combination of different techniques, technologies and artificial intelligence models to adapt the best solution to each need. It is recommended to avoid the use of a single model to solve all the functionalities of the project, thus ensuring greater effectiveness and customization of the results.
There are several complementary concepts and techniques to consider when developing a generative AI project. RAG (Retrieval-Augmented Generation) combines information retrieval with text generation to provide more contextual responses. Prompting guides text generation through specific instructions. Finally, Fine-Tuning adjusts a pre-trained model with specific data to specialize it for particular tasks, thus improving the accuracy and adaptability of AI systems.
Proprietary environments in one of the world’s largest Cloud providers to ensure information security.
No data sharing, as it is an isolated, controlled and secured environment.
AI Health uses a modular system of applications and simplifies the development of AI solutions.
AI Health brings intelligence to work teams, radically increasing productivity.
AI models typically have consumption-based pricing. For example, foundational LLM models charge for the use of tokens. To these costs must be added the costs of the project’s technology infrastructure and the consumption of machines for training the models.
The volume and quality of data are crucial for an AI project, as large amounts of accurate data enable more robust and reliable models to be trained, improving their ability to make predictions and informed decisions.
The integration of AI projects into corporate systems is essential to optimize operations, promote innovation and maintain competitiveness. This integration must be continuous and operational, facilitating constant improvement that enables AI to adapt to evolving business needs and improve efficiency and decision making.
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