Curiosity is an enterprise AI platform that creates a context graph to connect data across complex systems, enabling teams to find, reuse, and trust answers at scale. Unlike simple chatbots, Curiosity models structured data and relationships, allowing AI to operate on real enterprise context. It integrates with production systems, structuring complex data from sources like ERP, PLM, IoT, and CRM into a unified knowledge layer. This graph-based approach captures the intricate dependencies and semantics of industrial environments, making knowledge accessible and actionable.
Key features include automated data ingestion, semantic search, and AI-powered reasoning. Curiosity deploys AI into real workflows, going beyond chat to automate tasks such as root cause analysis, compliance checks, and knowledge reuse. It supports aerospace, defense, rail, mobility, energy, infrastructure, industrial manufacturing, and life sciences, handling domain-specific terminology and regulatory requirements.
Benefits include faster case resolution with full system context, early issue identification, reduced risk, and audit readiness. Users can trace root causes across data silos, connect knowledge across designs and decisions, and improve efficiency in production workflows. The platform is designed for scalability and security, operating in the customer's environment.
Use cases include resolving complex support tickets by surfacing relevant data from multiple systems, accelerating engineering decisions by linking design history with test results, and ensuring compliance by maintaining a traceable knowledge graph. Technical details: Curiosity uses graph databases and machine learning to build and maintain the context graph, with APIs for integration. It offers role-based access control and audit trails. Pricing is based on deployment scale and support level, with options for on-premises or cloud.
aerospace engineers, defense teams, rail operators, mobility providers, energy companies, infrastructure managers, industrial manufacturers, life sciences teams