Agentic AI for Manufacturing and Utilities

Manufacturing and utility organizations operate in an environment where downtime is expensive, safety incidents are catastrophic, and the knowledge needed to prevent both is buried somewhere inside decades of engineering documents, maintenance records, sensor data, and supplier communications — spread across systems that were never designed to talk to each other.

Airbus, Alstom, Siemens, and Volkswagen use Sinequa to solve this problem — giving engineers, maintenance teams, and R&D professionals instant access to the right knowledge, at the right time, regardless of where it lives or what format it’s in.

Four Use Cases That Drive Industrial Performance

• Optimize R&D and Production Finding existing technology components for reuse across a large manufacturer’s ecosystem of products, partners, and suppliers is often impossible without the right search infrastructure. Sinequa’s platform indexes technical product details, patent filings, scientific journals, engineering databases, and supplier records — enabling engineers to discover reusable components and subsystems, accelerating development timelines and reducing costs.
• Safeguard Knowledge and Know-How Manufacturing organizations lose critical institutional knowledge every time an experienced engineer retires or changes roles. Sinequa indexes people data alongside technical documentation — connecting employees to internal experts, legacy procedures, and historical project data before that knowledge walks out the door.
• Ensure Safety and Compliance Regulatory requirements in manufacturing and utilities — GDPR, PCI, SOX, ISO standards, and sector-specific safety regulations — generate enormous volumes of documentation that teams must navigate in real time. Sinequa surfaces relevant regulatory updates, compliance procedures, and audit documentation from a single interface, ensuring teams act on current requirements rather than outdated guidance.
• Improve Maintenance and Customer Service Complex products — aircraft, gas turbines, power stations, industrial machinery — require maintenance procedures that span decades of documentation. Sinequa provides 360° views of maintenance histories, current procedures, component designs, and inspection records, giving field technicians the information they need without searching through decades of unstructured files.

Key Platform Capabilities

• NLP and semantic search across structured and unstructured industrial data — PLM systems, ERP, MES, IoT streams, maintenance records, and technical documentation
• 360° views of assets, products, customers, and components across all connected systems
• Machine learning that improves relevance over time based on actual user behavior
• IoT and sensor data integration alongside slow-paced maintenance and regulatory records
• Logical data warehouse to structure, categorize, and enrich data for information-driven applications
• Enterprise-grade security with role-based access control inherited from source sys

Trusted by Global Industrial Leaders

Airbus deployed Sinequa to give more than 700 scientists located around the world a single, secure point of access to all structured applications, unstructured information, and people data — enabling real-time collaboration across engineering teams on multiple continents. Alstom used Sinequa to eliminate redundant parts manufacturing across a 3-million-part catalog, saving $40 million, plus an additional $6 million through automated proposal generation. Siemens reported 30% faster information discovery for technical teams. Volkswagen uses Sinequa to connect knowledge workers across its global manufacturing and R&D operations.

From Cognitive Search to Agentic AI in Manufacturing

Sinequa’s search foundation is what makes agentic AI in manufacturing trustworthy — AI agents that monitor equipment data and surface maintenance alerts before a failure occurs, or that automatically cross-reference a new regulatory update against existing compliance documentation. Every AI-generated answer is grounded in authorized enterprise data, not generic model outputs. For manufacturers building their Industry 4.0 AI strategy, that’s the difference between a system engineers trust and one they work around.