Why matching is harder than it looks
A tender position rarely names a product. It describes properties: material, dimensions, fire rating, norm references, installation context. The same requirement can be phrased in dozens of ways depending on the planner, the region, and the standard text library used.
On the other side, product data lives in ERP systems and PIM catalogs with their own logic: article numbers, attribute fields, marketing names. Between requirement language and catalog language sits a translation problem that has traditionally required years of product expertise.
The limits of search and rules
Keyword search finds products that share words with the specification, not products that fulfill it. Rule-based systems work until the next exception, and construction tenders are made of exceptions. Both approaches also fail silently: the wrong product looks just as confident as the right one.
How AI-based matching works
Modern matching systems combine three layers: extraction of structured requirements from the tender text, a semantic understanding of product attributes, and a ranking model that scores candidates by technical fit. Crucially, the system exposes its confidence, so experts review the uncertain cases instead of checking everything.
Data is the moat
Matching quality is a function of data quality. Suppliers who invest in clean, attribute-rich product data see compounding returns: every matched tender improves the system, and every improvement makes the next tender faster.
Our product data used to be a maintenance burden. Now it is the asset that decides how fast we can quote.
What comes next
Audit your product data, start matching on your highest-volume product lines, and keep experts in the loop. Matching at scale is not about removing people, it is about pointing their expertise at the cases that need it.