ince its founding in 2008, Decide has embedded artificial intelligence into its technological DNA.<\/strong> Unlike the often \u201chyped\u201d narrative around AI, Luengo stresses that \u201cit\u2019s not magic\u201d \u2014 but rather a technology that, when well designed, can transform data into complex patterns that enable efficient, measurable decisions aligned with business goals.<\/p>\n\n \u201cOur approach has always been pragmatic. If it doesn\u2019t solve a problem, it\u2019s not a solution. The key is applying AI where it delivers real value.\u201d<\/p>\n<\/blockquote>\n\n Agentic AI<\/strong><\/p>\n\n \u00c1ngel Luengo advocates for drawing a clear line between their approach and traditional workflows that rely on language models. According to reference frameworks from organizations like Anthropic and OpenAI, agents begin where rigid orchestration ends. It\u2019s a paradigm shift: it\u2019s no longer about executing step-by-step instructions, but about building systems capable of making their own decisions based on a defined goal. This philosophy translates into a layered architecture<\/strong> that Decide applies systematically. Designing in layers allows us to tailor the level of intelligence and autonomy to each use case, without losing control or adding unnecessary complexity,\u201d explains \u00c1ngel.<\/p>\n\n This modular approach has become a key competitive advantage<\/strong>, raising the success rate of pilots above 95%. In the energy sector, for example, systems have been developed to dynamically adjust prices at charging points based on demand and market conditions. \u201cOver 95% of our developments make it to production because, from the start, we combine AI, architecture, and business in a coherent and applied way.\u201d<\/p>\n<\/blockquote>\n\n \u201cThe key is to design every project from the ground up \u2014 with a clear purpose, a scalable architecture, and a strong business vision,\u201d emphasizes \u00c1ngel. <\/p>\n","protected":false},"excerpt":{"rendered":" ince its founding in 2008, Decide has embedded artificial intelligence into its technological DNA.“We\u2019re not a company that jumped on the AI bandwagon,” says \u00c1ngel Luengo, Head of AI at decide. “For over a decade, we\u2019ve been helping companies make complex decisions using models that learn from data and integrate seamlessly into their daily operations.”…<\/p>\n","protected":false},"author":30,"featured_media":32558,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[770,262],"tags":[801],"class_list":["post-32570","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tecnologia","category-uncategorized","tag-agentes-inteligentes","category-770","category-262","description-off"],"yoast_head":"\n
“We\u2019re not a company that jumped on the AI bandwagon,”<\/em> says \u00c1ngel Luengo, Head of AI at decide. “For over a decade, we\u2019ve been helping companies make complex decisions using models that learn from data and integrate seamlessly into their daily operations.”<\/em><\/p><\/p>\n\n\n
For \u00c1ngel, this has direct business implications: \u201cThe more agentic a system is, the more flexible and autonomous it becomes\u00a0 but also harder to audit, maintain, and govern. That\u2019s why it\u2019s essential to design with intent, and avoid applying agents where they don\u2019t belong. You should never delegate what you can automate,\u201d<\/em> Luengo explains.<\/p>\n\n
A first layer of semantic validation using lightweight models and deterministic processes to ensure contextual coherence and access control;
a second layer of dynamic orchestration based on embeddings and adaptive planning;
and finally, a third layer that incorporates agents designed to solve specific tasks, integrated into a traceable and auditable environment.<\/p>\n\n
Decide has deployed operational solutions across diverse sectors such as energy, banking, insurance, and retail \u2014 always applying AI where it truly delivers value.<\/p>
In banking, intelligent chatbots are used to authorize transactions in real time while complying with the most stringent regulations.
In insurance, language models analyze unstructured documentation to extract key information, powering business processes and automating back-office tasks.
And in retail, intelligent systems are used to forecast demand, optimize stock replenishment, and plan staffing more efficiently.<\/p><\/p>\n\n\n