The Center for Data Innovation recently spoke with Alyn Franklin, CEO of Oritain, a New Zealand-based company that uses advanced technology to verify product origins. Franklin discussed how Oritain integrates AI to detect anomalies in a product’s chemical makeup and provide insights that support the development of supply chain risk simulations, helping clients prepare for potential disruptions more effectively.
Eli Clemens: What does Oritain do?
Alyn Franklin: Oritain verifies a product’s origin by identifying its unique chemical characteristic, what we call its “origin fingerprint.” This fingerprint is shaped by environmental factors like soil, climate, and altitude and helps us determine whether a product really comes from where it claims to. We analyze these fingerprints using forensic and statistical methods, then store them in our global database. When we test new samples, we compare them against this database to confirm their origin. We specialize in several industries, particularly cotton, and are now expanding into high-risk commodities like coffee, wool, and cacao, sectors that are especially susceptible to supply chain opacity.
We’re increasingly integrating emerging technologies like AI and machine learning into our process. While our core verification relies on scientific analysis of the product itself, AI helps us work more efficiently at scale, identifying patterns across global supply chains, detecting subtle anomalies, and providing perspectives on potential sourcing risks. These tools allow us to continuously improve the accuracy and responsiveness of our system as supply chains evolve.
Clemens: How does Oritain verify a product’s origin when supply chains mix materials from different sources?
Franklin: As supply chains become more complex, products often combine materials from various places and regions, making verifications more challenging. Oritain addresses this by expanding our global database of origin profiles and refining our ability to detect when a product’s physical composition no longer aligns with its claimed source. We use machine learning models to strengthen this process, recognizing patterns within the data that match known origin profiles and flagging even subtle changes. As new data comes in, we retrain our models to account for shifting suppliers, regions, or seasonal factors. For example, if cotton from different regions starts being mixed together, our models can pick up on those changes early, even before they show up in supplier paperwork or other standard tracking tools, which may not capture granular blends.
Clemens: How does Oritain use AI to predict disruption or risks in sourcing before they happen?
Franklin: Beyond detecting sourcing changes after they happen, we also use AI to anticipate them before they cause problems. Our models analyze complex supply chain data to identify early warning signs, such as gradual shifts in sourcing regions or production patterns, and flag areas that may pose future risk. These insights allow us to simulate possible sourcing scenarios and assess how they might affect product authenticity and compliance. That foresight enables our clients to plan ahead, adjust their sourcing strategies, and avoid long-term supply chain disruptions.
Clemens: Could you provide some examples of Oritain’s work?
Franklin: Oritain has nearly two decades of experience in scientifically verifying product origin. Our partnership with the U.S. Customs and Border Protection (CBP) has highlighted the value of forensic verification in enforcing trade laws, such as bans on goods produced with forced labor. Through our government-focused team, Oritain Government Services, we support agencies in detecting and preventing counterfeiting, unethical sourcing, and fraud. This includes working with U.S. regulators like CBP and with regional bodies implementing measures such as the EU’s Deforestation and Forced Labor Regulations.
Traditional traceability tools often rely heavily on paperwork, which can be manipulated. By analyzing the product itself, we offer more robust evidence that supply chains meet legal and ethical standards. For example, we’ve helped fashion brands identify unauthorized cotton blends, worked with coffee suppliers to confirm compliance with regional sourcing requirements, and supported honey producers in proving authenticity, giving them a competitive edge in increasingly regulated markets.
Clemens: How is Oritain scaling its technology and data infrastructure to lead in forensic supply chain verification?
Franklin: Scaling our data infrastructure starts with coverage. Our mapping efforts have already captured complete coverage of all core cotton-producing territories, giving us and our clients an unrivalled view of the cotton market. We’re now applying the same approach to other major commodities like coffee and wool, extending our science-backed verification capabilities across more sectors and regions.
We’re also investing in the technologies and systems that support that growth, from how we ingest and manage large volumes of sample data, to how we structure and standardize material profiles across regions. We’re continually expanding our network of trusted laboratories and strengthening our global databases to maintain consistency across every industry we serve. This operational scale enables us to deliver reliable, scientifically-backed verification to a wide range of clients, while staying responsive to the shifting demands of the market. Ultimately, we remain grounded in our core mission: to support the protection of our planet, the well-being of people and animals, and the overall quality of life.
