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Dynamic Master Data Engine

ERP master data, regenerated by AI as business requirements change — instead of set once and maintained by hand.

The problem

ERP master data — material master, vendor master, customer master — is normally fixed. It's configured once during implementation, and after that, every change in business rules means someone manually updating records. Rules drift, data doesn't keep up, and master data quality quietly degrades over time. This tool closes that gap: it reads current business requirements and regenerates the relevant master data to match, instead of leaving that translation to a manual process.

How it works
01

Business requirements come in

A requirement — new pricing rule, new vendor category, changed procurement logic — is captured as input, in plain business language rather than a technical spec.

02

The engine interprets and maps it

An AI layer translates the requirement into the specific master data fields and records it affects, instead of a person tracing it through the data model by hand.

03

Master data is generated or updated

The relevant master data — material, vendor, or customer records — is created or adjusted automatically, kept in sync with the requirement that drove it.

Under the hood
business_requirements.input requirement A requirement B requirement C AI engine material master vendor master customer master ↳ updates as rules change

Requirements in, structured master data out — regenerated as the rules change, not maintained by hand.

Stack

LLM-driven generation ERP data model
Demo video