00Product data, engineered for AI
We raise the economic value of your product data — cleansed, harmonized, structured and proven usable for people and AI. Maturity is measured before and after the work, so progress stays verifiable.
01AI and data
AI is only as good as the data beneath it.
A model can only answer what your data can answer. Where attributes are not properly qualified, units contradict each other or values are missing, an assistant does not stay silent — it answers anyway, fluently and unfortunately wrong. To keep that from happening, get the data right first: a single source of truth (SSOT).
This is why we do not start with the models. We start underneath them, with the data and its refinement — that is what makes the difference in quality. Choosing the models and what to feed them comes after.
02The starting point
Product data lives scattered across ERP, spreadsheets, translation files, InDesign documents and shop systems. The result: contradictory specs per channel, slow and expensive catalog production — and AI assistants that return unusable answers because there is no consistent basis to draw on.
11 of 16 values deviate — 7 in notation only, 4 wrong or missing
03The approach
Everything flows in — from whichever ERP, file or system your data lives in today. Everything flows out — to print, web, further outputs and AI, from a single source of truth. In between sits a chain of refinement where every step is measurable and every change traceable. That is the difference between a hub and a place to store attributes.
Every source attached where it lives — no hand-migration, no lock-in. Proven connectors, plus REST and CSV for whatever else you run.
Duplicates, typos, dead units and orphaned records removed.
One notation, one unit system, one vocabulary across every source and every language.
Attributes become typed fields with ranges, units and dependencies instead of free text.
Products, variants, accessories and documents connected into one coherent model.
Rules run on every change. What fails never reaches a channel — or an AI.
The refined data leaves the hub: Print Engine and Product Finder read from it directly, shop, marketplace and other systems receive exports. Everyone works from the same state.
The same dataset becomes knowledge: product questions answered with a source, for your people and for AI agents.
04Hub and modules
Three parts, clearly separated. The data hub is the machine underneath: it connects your sources and refines them into one qualified data source. On top sit the modules that turn it into something people use — on the web and in print.
Every usable company data source is cleansed, harmonized, structured and modeled. What stands at the end is one qualified data source (SSOT), from which everything else is served.
Your customers find the right product on your website in a few clicks. Dedicated search and filter algorithms keep the site extremely fast even with tens of thousands of products — response times stay very short.
Adobe InDesign is the publishing engine, with plug-ins putting the hub functions right inside it — your people keep working in the environment they know, with AI assisting them in drafting pages and assigning products to them. What counts here is presentation quality: press-ready pages with complex tables and matrices, no mapping or the like required.
Print Engine = stage 2 “Publish”, Product Finder = stage 3 “Reach”, knowledge base = stage 4 “Intelligence”. All three read from the same hub. Shop, marketplace and other systems are served by export.
05The instrument
The Product-Data Maturity score rates your catalog from 0 to 100 across five dimensions and places it on six levels. Every point traces back to concrete products and attributes, so the number survives scrutiny. Move the dimensions to see how a catalog climbs.
Illustrative model. Your real score comes from a measured inventory of your own catalog.
06The build-out
One qualified data source instead of file islands.
Catalogs, price lists, downloads and datasheets straight from the source.
One defined dataset carries your website out into the world through the Product Finder — and supplies shop, marketplace and further systems.
Product knowledge that answers questions — for sales, service and AI agents.
07Urgency
The longer you wait, the more your baseline is gone for good.
The maturity gain you will want to prove needs a measured starting point — measured before the clean-up begins. Whoever starts tidying up first can never show the jump afterwards.
08Fit
We would rather tell you in the first call than in month six.
What we don't do: maintain your product data for you. The data is your asset, and your people know your products.
09Questions
In-house or in a hosting region you approve, under your control. This is product data, almost never personal data — which keeps the compliance surface small.
No. We are a data hub — the layer that brings your sources together, harmonizes them and serves every channel from one dataset. A PIM can be added if you want one — when your people need a maintenance front end. If you already run one, it stays and the hub reads from it.
No. The ERP stays the system of record for commercial data. The hub is the system of record for product knowledge, and it reads from your ERP.
Yes — at any time and while running, not only at the end. A structured export of your whole dataset, including the harmonization rules we built with you. The data is your asset; taking it with you is part of the product.
Out of many years in product data management, nothing was carried forward — something new was built, on the basis of years of expertise. A small, highly specialized team, in-depth industry knowledge and close cooperation, for medium-sized and large companies with complex, international product portfolios. We start where other e-commerce systems reach their limits. What we hold to: high data quality, short implementation times, fast applications, close cooperation.
A short assessment turns the question into a number.