The principal use case of the bigbox.bot is automation of Electronic Document Interchange (EDI) character set translations that meet or exceed compliance as set by law and contract in private sector, government, B2B and B2G trading partnerships with training emphasis in the areas of logistics and security. A resultant benefit of the bigbox.bot’s LLM training is its agentic data governance abilities for enterprise finance, manufacturing, distribution, and transportation systems including data sources behind secure proprietary endpoints in domestic and international supply chains.
The bigbox.bot will use Agentic AI. Its case purpose requires limited generative content or real-time chat communication for end users. Built specifically for domestic and international supply chain communication compliance, the bigbox.bot will execute according to strict rules governing Agentic AI decisions including all corresponding human interaction and communications.
Background:
EDI specifications are developed from standards derived from EDI Transaction Sets. A transaction set provides a template, or map, of required electronic document content. While maps act as a content guide, each document exchanged, or “traded”, varies by transaction set type, industry, and agreements between document trading partners. From purchase orders to invoices, electronic document interchanges fuel the consumer supply chain. Without them supply chains don’t run.
While following the standards set by an EDIFact or X12 transaction sets, most published EDI document specifications contain dissimilar characteristics including, for example, varying placement of elements (ex: customer_address) and character delimiters. These variations, based on unique supplier or customer requirements, result in correlations that are currently addressed using either a privately hosted EDI translation service, a local iPaaS platform, or translation by a 3rd party Value Added Network service, a VAN. Each of these solutions provides data transformation, allowing integration between varying data sources such as data lakes, warehouses, and SQL tables behind ERP, WMS ,TMS and 3PL systems.
Each of these translation options can be effective at passing document content into respective backend systems. This is at the expense of per document development for each type of document required in the transaction set. For example, a document for a Purchase Order (850) and another for an Invoice (810), and the most notorious, the Advanced Ship Notice or ASN (856). Per document testing is time consuming and expensive, often delaying initial orders as suppliers acquire new customers. For now EDI documents are required and cannot be ignored. They are the only generative product of the bigbox.bot’s initial LLM.
While passing data through current disparate platforms and processes, data discrepancies routinely result in manual corrections by EDI Analysts and Developers, who must work with secure internal data sources, team members, and too often senior management to correlate document contents for successful transmission. When processing documents against real time data sources the bigbox.bot’s AI agents will be faster at identifying EDI errors and alerting the correct human contacts of any remedy. Optionally, the future bigbox.bot LLM could perform requested corrections at subscriber data end points through agentic edge hosts, adding security efficiency, compliance, and cost reduction in the international supply chain.
The bigbox.bot will learn document specifications, logistics and compliance
requirements by training a private Large Language Model or LLM with secure
Agentic AI agents, using existing EDI document specifications, 2nd and 3rd party
data currently in use by major industry and logistics companies. It will also rely
in-part on crowdsourcing and industry contribution for a large amount of EDI
specifications in the retail and logistics sector now available to vendors.
Healthcare and Government suppliers might share some of the same document
specifications as private sector trading partners, but have their own document
series specifications that will require comprehensive security and compliance
reviews prior to any bigbox.bot version release.
Initial deployment of the bigbox.bot will include secure Agentic AI agents
deployed to U.S. Domestic and international retail trading partners under existing
Vendor IDs for the 800 and 700 series EDI document sets using specificationsfrom U.S domestic retailers and customizations per vendor integration. It will
also preserve historic Interchange Start Acknowledgement IDs (ISA) and
Interchange End Acknowledgement IDs (IEA) including corresponding document
segments to minimize any impact of conversion from legacy translation services
and systems to the bigbox.bot. Support for lower 200 series Logistics and upper
200 Healthcare EDI specifications will deploy with future bigbox.bot releases.