Context Labs: Bringing Context to Connections

Our methodology is one that incorporates five distinct layers in our software stack. First, we begin by virtually ingesting all relevant data associated with that customer’s use case and begin preprocessing the data. Following that, we proof the data using a branded product called ProofWorks™. This serves as one of our key differentiators that mathematically attest to the source identity, data itself, and any associated algorithms and trusted partners. Then, the data is ledgered on either our own blockchain ledger, Scrivener™or integrated onto another ledger using our open API system. Then, we layer in cutting- edge network graph analytics to provide both descriptive and predictive insights for that customer's use case. Finally, we provide any visualization, manipulation, dashboarding, or other presentation to serve the information to the customer and provide them with the highest quality, most immutable, and attestable information about their use case, with our Impact Analytics™ components.

Could you cite one or two case studies on how you have enabled clients to overcome hurdles and attain desired outcomes with your innovative array of solutions?

A great customer example is RR Donnelly, one of the largest book printer firms globally, and their spin-off company Lakeside Communications. Piracy, velocity, loss, damage, and other revenue reducing problems plagued the book publishing supply chain. We implemented and integrated our Immutably™ solution for them in their book publication supply chain, enabling them to reduce fraud and save their customers money. We effectively made a physical/analog book "digital," by giving every book its own cryptographic identity, its "digital twin," allowing it to be tracked from printing to distribution, and on into a consumer's hands.

We have the ability to not only transform physical/analog products with digital representations, but we also do it with data. An excellent example for this in a customer-enablement scenario was the Environmental Defense Fund (EDF). Using Immutably™ and Impact Analytics™, EDF was able to model every existing wellhead in the state of Pennsylvania, the reported emissions that came from them, while also layering in publicly available population health data to begin to describe the correlation be-tween emissions and health. Our analytics and modeling, rooted in their existing models, helped to highlight the massive differ¬ence between the emissions reported by the industry operators and the actual emissions from each wellhead while predicting the emissions level in several different proposed policy implementa¬tions. This important environmental data has now been secured at its source by using Immutably™, and Proofworks™ is ensuring the veracity of the data throughout its life and utility.

In addition to OMI, you’ve also spurred the conception of a couple of notable public initiative solely on the concerned industry’s imperative need for supply chain integration. Could you elaborate more on the same?

We co-founded the Mobility Open Blockchain Initiative (MOBI, www.dlt.mobi) with critical leaders and major companies in the automotive industry to bring blockchain solutions into vehicles for a more resilient, cleaner future for transportation. Lots of many great announcements are coming in the future not just for Context Labs and Spherical|Analytics, but also for our industry partners at the OMI and MOBI.