GRID#26 Recap — Poland’s First Product Data Conference Just Confirmed What 70+ PIM Implementations Already Told Us
On May 7, 2026, around 200 people sat down in Centrum Manggha in Kraków for the first Polish conference built entirely around product data. Ergonode organized it. openProd.io was the Main Partner. I had 30 minutes on stage. I used them to explain why every PIM project we’ve shipped has hit the same wall, and what we built to break through it.
GRID#26 mattered for a reason that has nothing to do with sponsor logos. The Polish market has not, until now, had a venue where PIM managers, data engineers, e-commerce directors and integrators sit in the same room and talk about the layer of work no software vendor really wants to own: getting messy supplier data into PIM-ready shape.
That layer is where the next decade of product data infrastructure gets built. Here is what we showed in Kraków, what the audience told us back, and what it means if you’re running a PIM in 2026.
The thesis we brought to the stage
PIM is not the problem.
That sentence is heretical inside the PIM industry, because the PIM industry sells PIM. But after 15 years at LemonMind, more than 70 implementations and roughly 40 million SKUs in production across Pimcore, Akeneo and Ergonode, the pattern is clear. The PIM works. What breaks is everything that happens before data lands in it.
A typical mid-market manufacturer or distributor receives supplier data the way the postman delivers junk mail. PDF spec sheets with embedded tables. Excel files with merged cells and three header rows. CSV exports missing half their columns. Photos of product labels where the ingredients of an overpriced face cream live in 7-point type. The first deal-breaker is getting any file at all.
When the file finally arrives, the supplier’s data model doesn’t match yours. Different attribute names. Different units. Different categories. Different language. Someone has to read the file, understand the product, map it to your taxonomy, decide what’s a variant and what’s a base product, and clean the whole thing before a PIM can do its job.
That someone is usually a category manager who costs €4,500 per month and takes three months to onboard one supplier with a few thousand SKUs. Multiply by the 10 to 50 new suppliers a real distributor takes on each year and the bill lands somewhere between €135,000 and €675,000 annually. That’s the cost of doing nothing. It does not count the revenue lost while products wait in a queue to be listed.
This is the “Excel hell” we keep flagging. It scales linearly with supplier count, which means it does not scale at all.
How the architecture works
We walked the room through the four pieces that make this solvable.
Multi-agent pipelines, not a single large model. Different file formats and different data structures need specialized agents talking to each other, orchestrated by a conductor agent. One LLM with a million-token context window cannot do this job reliably. We tried. It fell over.
Confidence scores on every field. The score is the difference between “AI did the work” and “AI did the work and told you where to look.” Without it, you’re back to manual review of every field. Which is the problem we started with.
PIM-agnostic. The same pipeline that pushes into Pimcore Studio via a native bundle also speaks REST API to Akeneo, Ergonode or any custom catalog. That was a deliberate architectural choice, and on GRID#26 it mattered. The room used different PIMs. The story still landed.
The whole flow comes down to one sentence. AI proposes. You decide. Nothing reaches the PIM without your sign-off.
The ROI table the CFO actually wants
Here is the second column of the business case we showed on stage. It’s the only column most decision-makers care about.
| Metric | Manual onboarding | openProd.io |
|---|---|---|
| Time per supplier | 3 months | 2 days or less |
| Labor cost per supplier (1 category manager) | €13,500 | €200 |
| Annual cost at 10–50 suppliers/year | €135,000 – €675,000 | €2,000 – €10,000 |
| Time savings | baseline | up to 95% |
| Error model | Human variability | Confidence scores per field |
| Scaling profile | Linear (more suppliers = more headcount) | Compute (more suppliers = more API calls) |
The shift from headcount-linear to compute-linear is the structural change. Every PIM manager in the audience understood it immediately, because every PIM manager has had the conversation with their CFO about why next year’s hiring plan grows in proportion to the supplier pipeline.
What the room told us back
A presentation is a one-way medium. The exhibition floor is where you find out whether the thesis lands. Here is what we heard at the openProd.io booth across the rest of the day.
People walked up with their own files. Not hypothetical files. PDFs from real suppliers, opened on a phone, dragged onto a laptop. “Run this.” That is the strongest signal a product-led demo can get, and it happened over and over.
Ergonode users wanted to talk. This was the warmest pocket of the audience. Ergonode is good PIM software. The data going into it is still the problem its customers face. openProd.io is PIM-agnostic on purpose, so we can serve users of any PIM with the same upstream pipeline. Several of those booth conversations are already in our sales pipeline.
Competitors showed up to look. That’s also a signal. The “AI Product Data Middleware” category we’ve been pushing since February is starting to be visible enough that the people building adjacent products want to see it in person.
A category is forming, in public
Most of the AI noise in product data right now sits at the protocol and integration layer. Useful, but increasingly table stakes. What does not commoditize as fast is the onboarding layer upstream of the PIM. That is the moat. Not the agent. Not the LLM. The understanding of supplier data formats. The multi-agent orchestration that handles them. The confidence scoring. The connectors into specific PIM data models. The human-in-the-loop UI that lets a category manager review a handful of fields instead of hundreds.
GRID#26 was, honestly, the first public Polish gathering of the category. Ergonode hosting it was the right move. The Polish market is roughly six months ahead of where the German and Dutch markets will be on AI-native product data infrastructure, because Polish distributors and retailers have lived with cross-language, cross-supplier complexity for years. The audience knew the problem before we said the word “AI.”
For openProd.io, this confirmed what Pimcore Inspire in Salzburg confirmed three weeks earlier and what the Infoshare 2026 Startup Contest will likely confirm next week in Gdańsk: there is a real, fast-forming category here, and the people who have been quietly fighting Excel hell for the last decade are ready to buy a way out.
What to do if you were in the room (or wish you were)
If you’re a PIM manager or data lead at a mid-market or enterprise company, and the numbers in the ROI table above look like your operating reality: bring us your worst file. Whatever the supplier sent you last week. PDF, Excel, CSV, image scan of a label. The uglier the better.
The fastest way to find out what openProd.io will do on your data is to put a real file through it. We will run the demo against your own product model and you will see the confidence scores, the cost and the import plan against your taxonomy, not a generic one.
Book a 30-minute demo at openprod.io/book-demo
Same offer we made from the stage in Kraków. Same offer we’ll make from the stage in Gdańsk next week. The PIM is not the problem. Now there is something you can do about everything that happens before the PIM.
LemonMind data: 70+ implementations, 15 years, roughly 40 million SKUs in production. We’ve seen what’s coming. We built openProd.io so you don’t have to.
