MatchMaker AI — five systems, one truth

Problem

Five source systems, five naming conventions, zero shared keys.

Approach

Designed, named, and built MatchMaker AI end to end: an ML-driven matching engine that unifies data from five oil & gas source systems - ERP, reserves, production accounting, drilling operations, and scheduling - into one cross-reference backbone. Built with Snowflake notebooks and Python AI/ML over a fact-dimension model, it powers unified volumes reporting views that were previously impossible because no two systems agreed on what a well was. From concept through production: the algorithm, the model, the reporting layer.

Architecture

Entity matching is the unsolved problem of energy data

Five systems, five naming conventions, zero shared keys. Deterministic joins get you 70% of the way; the last 30% is where reporting dies. MatchMaker applies ML-based matching to the remainder - scoring candidate matches, learning from confirmations - so the cross-reference converges instead of rotting. The unified reporting it enables is the payoff; the matching engine is the moat.

Stack

• US Oil and Gas Operator
• Snowflake
• Machine Learning
• Python
• Data Modeling
• Master Data Management

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