We set out to stop solving the same problem manually, every single time.
For over two decades, our network has lived inside Oracle supply chain implementations — Manufacturing Cloud, Inventory Cloud, Product Data Hub, Supply Planning, Costing, Warehouse Management — for organizations ranging from global aerospace manufacturers to Fortune 500 industrials to hyperscale technology companies managing hundreds of facilities worldwide.
Every one of those engagements eventually hit the same wall: the data conversion.
On one of our most recent engagements — a full SCM Cloud transformation for a major steel manufacturer — we led the data conversion effort ourselves: item master, item cross-references, on-hand quantities, blanket agreements, purchase orders. We know, first-hand and recently, exactly what that work actually looks like. The duplicate item records hiding behind slightly different descriptions. The cross-reference that points to a supplier site nobody set up yet. The blanket agreement that looks fine until you try to convert the purchase orders sitting underneath it.
We've done this same work — Manufacturing, Inventory, Costing, PDH, Planning — across aerospace, industrial manufacturing, telecom, consumer goods, and technology infrastructure. Different industries, same failure patterns, project after project.
So we built the tool we wished we'd had.
We didn't set out to build a product company. We set out to stop solving the same problem manually, every single time.
MigrateIQ started as an internal accelerator — a way to encode the pattern-recognition we'd built up over 20+ years of hands-on Oracle data conversions into something that runs in minutes instead of days. It has grown into a real AI-powered platform — one that understands the same object model we've configured by hand for two decades: items, bills of material, work orders, suppliers, purchase orders, and the dependencies between them.
We're still the same network of Oracle experts. But the judgment that used to live only in a consultant's head during a data conversion — the instinct for "this item number looks wrong" or "this reference won't resolve" — is now built into software. You get it in days, not weeks.
Manufacturing, Inventory, Planning, PDH, and Costing implementations across aerospace, industrial, telecom, and consumer goods — including leading data conversions by hand, engagement after engagement.
Duplicate records, broken cross-references, hidden parent/child dependencies — the specific patterns repeated so consistently across clients that they stopped feeling like edge cases and started feeling like a system waiting to be built.
The pattern-recognition, productized — an AI engine that finds and explains the same issues our consultants used to catch manually, in minutes instead of days.
The same engine, pointed at reporting and cross-ERP integration — and every new ERP object and capability after that. Not a fixed roadmap. An architecture built to keep growing.