Product Traceability

AI-driven product tracking enhances supply chain transparency, compliance, and consumer trust.

Business impact

• Supply chain efficiency — Streamlines operations by providing real-time product tracking and reducing delays
• Inventory accuracy — Ensures precise stock levels through verified product movement and status updates
• Customer trust — Builds confidence by providing transparent product origin and authenticity information
• Regulatory compliance — Facilitates adherence to laws by validating supply chain data and certifications
• Risk mitigation — Identifies and addresses supply chain disruptions and counterfeit risks early

Data requirements

• Supply chain transaction records (Structured) — Used to track product movements and status updates across partners
• RFID and IoT sensor data (Numeric) — Provides real-time location and condition monitoring of products
• Blockchain ledgers (Structured) — Ensures immutable and verifiable traceability data across the supply chain
• Satellite imagery (Image) — Supports agricultural product origin verification and risk prediction
• Product certification documents (Text) — Validates compliance and ethical sourcing claims

AI methods and techniques

• Predictive AI — Forecasts supply chain risks and disruptions based on historical and real-time data
• Generative AI — Automates document classification and data extraction from unstructured sources
• Symbolic AI — Applies rule-based validation for compliance and traceability logic

AI models and model families

GPT-4o, Claude, Llama, Custom ML models for supply chain analytics

Industries

Real-world evidence

8 documented case studies on record.

Companies using this: Agri Task, Connecting Food, Gap, H & M, Inspectorio, Moncler, Nars Cosmetics, Tilkal.