--- title: "Italian Enterprises Leading in AI" description: "AI models depend on flexible, high-performance data infrastructure. Leading Italian enterprises including TeamSystem, altermAInd, Brembo Solutions, and Eni are using MongoDB Atlas to power their AI initiatives. TeamSystem built a centralized AI platform with hybrid search combining full-text and vector search that delivers precise, context-aware results while minimizing hallucinations, scaling from 50,000 to 500,000 users and cutting task times by 30–40%. altermAInd solved GDPR and EU AI Act compliance at the database layer, enabling secure innovation for over 200,000 customers. Brembo Solutions uses ALCHEMIX to slash R&D formulation from months to minutes by leveraging MongoDB’s document model for diverse material data. Eni uses Atlas Search and vector search to instantly analyze years of geoscience data and Atlas Charts for real-time interactive visualizations without hand-coded dashboards. These companies show that a robust, secure, and scalable data platform is foundational to enterprise AI success." --- # Italian Enterprises Leading in AI [MongoDB](https://yomu.fyi/company/mongodb) · Oct 15, 2026 **Type:** Problem & solution ## Summary AI models depend on flexible, high-performance data infrastructure. Leading Italian enterprises including TeamSystem, altermAInd, Brembo Solutions, and Eni are using MongoDB Atlas to power their AI initiatives. TeamSystem built a centralized AI platform with hybrid search combining full-text and vector search that delivers precise, context-aware results while minimizing hallucinations, scaling from 50,000 to 500,000 users and cutting task times by 30–40%. altermAInd solved GDPR and EU AI Act compliance at the database layer, enabling secure innovation for over 200,000 customers. Brembo Solutions uses ALCHEMIX to slash R&D formulation from months to minutes by leveraging MongoDB’s document model for diverse material data. Eni uses Atlas Search and vector search to instantly analyze years of geoscience data and Atlas Charts for real-time interactive visualizations without hand-coded dashboards. These companies show that a robust, secure, and scalable data platform is foundational to enterprise AI success. ## Context Across industries in Italy, enterprises are adopting AI to transform their businesses. However, legacy databases often fail to handle the fluid, dynamic nature of AI data, creating bottlenecks in scalability, security, and performance. Four Italian companies—TeamSystem, altermAInd, Brembo Solutions, and Eni—faced such challenges and turned to MongoDB Atlas as their data infrastructure foundation to enable flexible, secure, and high-performance AI applications. ## Approach / What changed The common approach is adopting MongoDB Atlas as a cloud-native data platform. It provides a flexible document model to store unstructured and complex data, supports hybrid retrieval (full-text search, metadata filtering, and vector search) for precise AI context, enforces strict multi-tenant isolation and access controls for compliance, and enables real-time query performance and scalable managed services. This allows each enterprise to build secure, compliant, and scalable AI solutions without managing infrastructure. ## Takeaways - Combining full-text search, metadata filtering, and vector search into a hybrid retrieval engine can deliver precise, context-aware results while minimizing AI hallucinations, as demonstrated by TeamSystem. - Solving regulatory compliance (GDPR, EU AI Act) at the database layer enables enterprises to innovate rapidly without compromising security or facing regulatory backlash, as altermAInd showed. - Using a flexible document model to handle diverse, unstructured material data can drastically accelerate R&D cycles—for example, Brembo’s ALCHEMIX reduced formulation development from months to minutes. ## FAQ ### How can AI applications ensure data isolation in multi-tenant environments? TeamSystem built a centralized AI platform on MongoDB Atlas that enforces strict data isolation and security for each customer while supporting over 2.5 million users across diverse business products, ensuring each tenant's data remains separate and compliant. ### Can AI innovation coexist with strict EU regulations? Yes. altermAInd used MongoDB Atlas to process massive real-time data while enforcing access controls and schema validation at the database layer, enabling compliance with GDPR and the EU AI Act without compromising innovation speed or performance. ### Why do legacy databases limit AI development? Legacy databases cannot fluidly handle unstructured, complex, and rapidly evolving data that AI models require. MongoDB's document model provides flexibility, real-time performance, and scalability, as shown by Brembo and Eni, allowing AI to process diverse data without friction. **Tags:** [AI](https://yomu.fyi/topic/ai), [Data Platform](https://yomu.fyi/topic/data-platform), [Enterprise AI](https://yomu.fyi/topic/enterprise-ai), [MongoDB Atlas](https://yomu.fyi/topic/mongodb-atlas) - Source: [MongoDB](https://www.mongodb.com/company/blog/innovation/italian-enterprises-leading-in-ai-built-on-atlas) - Source URL: https://www.mongodb.com/company/blog/innovation/italian-enterprises-leading-in-ai-built-on-atlas - Ingested by Yomu: 2026-10-15T15:00:19.036Z [Read original post](https://www.mongodb.com/company/blog/innovation/italian-enterprises-leading-in-ai-built-on-atlas)