Value Chain Data Agglomeration and Heap

LionRT enhances your existing interfaces to 3PLs and gives you the tools to create new ones.


Graph databases are composed of nodes that build intramural (i.e. intra-organizational or within organization) and extramural (i.e. extra-organizational or outside of organization) relationships. LionRT applies each performance relating to one value chain with transactions that create the relationships between organizations. In this manner, the complex and intricate web of 3PL partners and their quality of performance are made readily available and transparent to organizations related to one another for handling of a value, be they goods in a supply chain or public safety in regulations. The number of use cases are limitless for this general purpose IoT infrastructure and aggregation blockchain.

Each performance in the LionRT graph database represents one organization under contract that is monitored for the quality of handling based on environmental parameters measured, recorded, and transmitted by wireless sensor networks. The transaction is the point when the transfer of goods and responsibility shifts from one organization to another. In the case of Last-Mile Logistics (LML), the transfer is made to the customer or a point of access such as a smart locker.

Under the hood, each piece of the performance is connected to the items surrounding it. This results in managing highly complex connected data, and performing complex queries.


LionRT accomplishes all of it's functionalities through digital contracts. These are agreements and promises of performance relating to specific goods that each have their own guidelines and conditions for handling. These conditions include terms that are the basis of team warnings and notifications, automated business responses, and machine automation responses that are defined in the contracts themselves.


The smart contracts under management are analyzed retroactively or in Real-Time for exception management. If the system you are governing does not collect data in Real-Time we provide you the solutions for just about any value chain that is facing such challenges. We build and train data analysis models for most any unique circumstances, and offer a variety of general exception monitoring instruments in the fields of artificial intelligence and machine learning. Exceptions have consequences, and our system is designed to handle performance disputes with the quality evidence and analysis to withstand enforceability of compliance measures and agreements.