Akeneo remains the trusted product record. MCP makes that trusted data useful everywhere else.
Table of Contents
Industrial and technical manufacturing
Make complex product data accessible across teams without relying on fragile, development-heavy data pipelines
Use Akeneo MCP to connect trusted PIM data with Fabric, Power BI, AI-assisted content workflows, SharePoint, and AWS
Product management, marketing, SEO, sales operations, and product content
Automated daily data refreshes, faster visibility into catalog gaps, and an acquisition workflow that can prepare a product target in about 40 minutes …
Automated daily data refreshes, faster visibility into catalog gaps, and an acquisition workflow that can prepare a product target in about 40 minutes
For a manufacturer managing complex technical products, multilingual content, frequent acquisitions, and millions of possible product configurations, giving every team fast access to reliable product information is not a small data project.
DwyerOmega is using Akeneo’s Model Context Protocol (MCP) to connect product information from its Akeneo PIM to Microsoft Fabric, Power BI, AI tools, SharePoint, and AWS. The result is a growing operational layer that makes product data easier to access, analyze, improve, and activate.
Instead of asking every product manager or marketer to learn another system, DwyerOmega brings governed product information into the tools teams already use. Behind the scenes, MCP also helps the company replace development-heavy integrations with more flexible workflows for data extraction, content enrichment, quality monitoring, and acquisition onboarding.
DwyerOmega had already established the Akeneo PIM as the source of truth for product information. The next challenge was making that information useful to teams with different responsibilities, habits, and technical skills.
Cyrus Grimes, Director of Product Content Management at DwyerOmega, had been extracting enrichment data through API connections to build Power BI dashboards. The approach worked, but it carried a cost.
The idea was that I wanted to be able to make dashboards for others to see enrichment levels of products, because the challenge was that not everybody wanted to go into Akeneo. So I was taking the data to where they live, which is Power BI.
Product managers run business units and balance many priorities. Marketing and SEO teams need to know where descriptions, metadata, images, and supporting content are missing. Sales operations need visibility into available catalog SKUs. These stakeholders benefit from Akeneo data, but they do not all work in the PIM every day.
“The very concept of opening another software program to access information, I felt, was a blocker.”
The pipeline itself was also reaching its limit. Multilingual content compounded the volume of every full load, and the extraction into Microsoft Fabric was not stable enough to support the next stage of growth.
It was not structurally stable in Fabric, and so I needed to rebuild my whole pipeline from Akeneo into Fabric. And I wanted to do deltas instead of full loads, because we were now doing multi-language and so the amount of data was compounded.
Using Akeneo MCP with Codex inside Microsoft Fabric, Cyrus rebuilt the pipeline around a structured table design. MCP lets the AI-assisted development environment work with Akeneo and Fabric through a single interface, explore the catalog structure, create the required tables, and troubleshoot the extraction.
Using one interface, I was able to work with two different tools at the same time and make them work together in a seamless sort of way, in a way that was very painless for me. It built all the tables, built everything I needed, and fed the information from Akeneo. Now it's a stable, self-updating system.
The new pipeline captures changes through delta updates rather than rebuilding the full multilingual dataset. It runs automatically every morning, and a full export across all languages completes in approximately 2.5 to 3 hours without hitting concurrency limits.
MCP is not replacing Akeneo as the governed source of product information. It is helping DwyerOmega extend that trusted data into the company’s broader analytics and operational environment.
DwyerOmega is testing richer model charts for products with extreme complexity, including one model with an estimated 100,000 to 200,000 possible permutations, and looking at ways to make Akeneo the governed source for AI agents that need accurate product context.
For Cyrus, the underlying value is structural.
MCP removes the borders between systems like Akeneo, Fabric, SharePoint, Jira, and Confluence, and lets them function together as one environment.
The long-term opportunity is bigger than any single dashboard or automation. Instead of building another isolated integration for every new need, the company can create repeatable workflows around one trusted product foundation.
For other manufacturers, the lesson is practical: the value of governed product data grows when people and systems can use it without introducing another layer of inconsistency. DwyerOmega is showing how MCP can turn a PIM from a destination for product data into an active source of operational intelligence.
Where that foundation gets especially interesting is what DwyerOmega can now build on top of it. From giving teams greater visibility into product data quality to accelerating content creation and onboarding newly acquired product lines, MCP is beginning to change how product information moves across the business.
In Part 2, we’ll explore those workflows and the impact they’re already having.
Our Akeneo Experts are here to answer all the questions you might have about our products and help you to move forward on your PX journey.
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