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Fenix Commerce Launches Incrementality Engine... Learn More

🧱 Before You Build with GenAI, Get Your Data House in Order 

At Fenix Commerce, we’ve been thinking a lot about Generative AI — not just what it can do, but what it demands from your organization in order to deliver on its promise. 

Everyone wants to plug LLMs into their workflows. But here’s a truth we’ve seen over and over again, especially in eCommerce: 

Most data is not AI-ready. 

🧩 eCommerce Data is Messy by Nature 

If you’ve ever tried to do anything ambitious with eCommerce data — you already know. 

Data from Shopify, BigCommerce, Salesforce Commerce Cloud, or Magento doesn’t just fall into your lap in a usable format. It comes through webhooks, APIs, and platform exports — often nested, unstandardized, or inconsistent in how it defines even the basics (like fulfillment status or timestamps). 

Then there’s the relational chaos: 

  • An order needs to be linked to a customer 
  • A SKU must match the product catalog 
  • A shipment needs to reconcile with inventory snapshots, carrier scans, and warehouse logic 

AI can’t guess its way through that. 

🏗️ The Real Work: Data Modeling, Not Just Cleaning

The process of getting AI-ready isn’t just flattening JSON sor mapping columns. It’s about defining a data model that serves your current roadmap: 

  • Which fields do you store? 
  • At what frequency do you ingest and update them? 
  • How do you handle partial events? 
  • What do you keep? What do you purge? 

For most companies, this data model was built years ago — usually to serve BI dashboards or basic order tracking. 

But now we want real-time predictions. Prompt-based interfaces. Auto-generated alerts. The old model can’t stretch far enough. 

🔁 At Fenix, GenAI Is the Excuse We Needed to Rebuild 

We see our GenAI development — including some very exciting new products we’ll share soon — not just as an opportunity to layer intelligence over existing data… 

…but to rethink the entire foundation

That means: 

  • 🔔 Rewriting our alerting logic 
  • ⏱️ Rebalancing refresh cadence by data source 
  • 🧹 Revisiting deletion and retention policies 
  • 🔄 Rebuilding how fulfillment data moves from ingestion to analysis 

And yes — re-architecting the data model itself, from the ground up, to reflect the questions our customers are asking today (not the questions we thought they’d ask five years ago). 

🧠 GenAI Isn’t Magic — It’s Leverage 

The potential of GenAI is incredible. But it can’t reason through chaos. If your underlying data isn't reliable, complete, and well-structured — the model will reflect that. 

That’s why we treat data modeling as a product in itself. 
And if you’re serious about building with AI, you should too. 

🚀 Want to learn how we’re building prompt-native delivery intelligence and real-time fulfillment predictions?

Let’s talk.

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Author: Tobi Konitzer,  PhD
Chief Innovation Officer, FenixCommerce

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