AI Infrastructure for Physical Object Intelligence

Enterprise-grade architectures for object recognition, counterfeit detection, authentication, quality control, and visual object analytics across global luxury and industrial ecosystems.

AuthenTrace is a deep-tech infrastructure company designing and deploying enterprise-grade AI architectures for physical object intelligence. Its systems enable object recognition, counterfeit detection, authentication, quality control, and visual object analytics at corporate and ecosystem scale.

A unified enterprise framework for long-term deployment

AuthenTrace delivers a unified AI framework rather than isolated technical services. The architecture is engineered for long-term enterprise deployment and supports governance, compliance, operational scalability, and continuous system evolution.

• Governance & auditability (traceable decisions, model/version control)
• Compliance alignment (deployment constraints, data handling, oversight)
• DPP compliance foundation (digital twin / passport-ready data layer for EU Digital Product Passport requirements)
• Operational scalability (multi-site rollout, heterogeneous capture devices)
• Continuous evolution (drift monitoring, controlled updates)

One foundation supports multiple deployments across brands, boutiques, CPO ecosystems, and marketplaces—preserving data continuity and compounding value over time.

Applications at ecosystem scale with built-in scalability

AuthenTrace architectures are designed for horizontal and vertical scalability across object classes, brands, markets, and geographies. Core representations, analytics pipelines, and governance components are reused and adapted, enabling expansion with limited marginal cost.

• Brand & manufacture protection — counterfeit detection, forensics, and controlled decision trails
• Certified pre-owned ecosystems — authentication, provenance continuity, and operational analytics
• Boutique & maison analytics — ecosystem visibility beyond classical inventory systems

Research-driven methods, enterprise-ready systems

From Autonomous Detection to Human-AI Collaboration: the system is built to augment artisans and experts, not replace them. Fully autonomous decisions are treated with caution in high-stakes authentication and are reserved for high-confidence cases.

• Patented AI methods for physical object fingerprinting
• Advanced representation learning for real-world objects
• Research-driven development from academic and industrial R&D
• Continuous refinement of models, pipelines, and architectures
• Proprietary physical-object datasets (reference libraries and operational captures)
• Agent-native workflow infrastructure (multi-party orchestration across buyers, sellers, and regulators)
• Moat: domain-specific data, deterministic controls, and an agent-native backbone for complex multi-party workflows between buyers, sellers, and regulators.

Deep research is packaged as deployable infrastructure: controlled lifecycle, traceability, and enterprise-grade reliability.

Discuss architecture and deployment

AuthenTrace engages with manufacturers, groups, and institutions through architectural design, system deployment, integration support, and pilot-to-enterprise frameworks.

• Architecture design: constraints, governance, risk boundaries
• Deployment: on-premise / hybrid / service-oriented
• Integration: capture devices, data flows, operational processes
• Operationalization: monitoring, drift, audit trails, lifecycle

Contact: info@authentrace.ch