A maintenance buyer photographs a worn component and uploads it to a supplier's portal. The AI returns a neat row of similar products. One is the right shape but the wrong dimension. Another is visually almost identical but belongs to a different equipment revision. The demo feels impressive until the buyer presses add to quote.
For a Dutch wholesaler, component distributor or technical ecommerce business, visual search can solve a real discovery problem. Customers often know what a part looks like before they know its catalogue number. The development opportunity is to turn that starting point into a defensible product selection, with a clear boundary between a candidate and a verified replacement.
Product identity still does the commercial work
GS1 Netherlands describes identifiers and data-sharing services for products, locations and organizations. Those identifiers help systems refer to the same trade item across a supply chain. They do not by themselves declare that two different items can substitute for one another.
For the search side, Google's official Product Search documentation shows retrieval of visually similar catalogue products, including scores and label-based filtering. Its product-search backend tutorial illustrates a catalogue built from reference images. These are examples of available capabilities, not a recommendation that every industrial catalogue fits that particular service.
Our engineering conclusion is to keep image retrieval, product identity and compatibility rules separate. That allows the business to evaluate each layer rather than treating the model's ranking as the final sales decision.
Ask which uncertainty the photograph can resolve
A photograph may reveal a product family, connector shape or visible label. It may not reveal an internal material, exact tolerance or supported firmware revision. Write down those limits for the initial product category before selecting a model.
The user journey can ask for one additional piece of evidence when it matters: a nameplate, a measurement or an equipment model. This is often more useful than displaying ten additional near-matches. The question should follow from the catalogue's decision rules, not from a language model improvising what sounds technically plausible.
For safety-critical or tightly specified parts, define where specialist review is mandatory. The portal can organize the enquiry without claiming to certify the choice. A customer should understand whether the result means visually similar, matches supplied specifications or approved by the supplier for the stated use.
Build an explicit substitution record
If your business offers approved alternatives, store the relation between the original item, the alternative and the conditions under which it applies. Include who approved it and the relevant version of the product information. A free-text note saying equivalent is too weak when the replacement only works with a particular adapter.
An illustrative catalogue might contain two similar fittings with different pressure ratings. The image system can retrieve both. A separate rule excludes one once the required specification is known. If the customer has not supplied that specification, the portal should ask rather than select the cheaper item automatically.
Keep discontinued products searchable when they are useful identification references, but distinguish them from orderable stock. An old photograph can help identify what the customer has even when that exact item is no longer sold. The next step becomes a controlled replacement enquiry.
Your image catalogue needs an update process
Product images change, supplier feeds arrive late and duplicate records accumulate. Connect every indexed image to the catalogue version and item identity it represents. When a product is withdrawn or a compatibility decision changes, the search and quotation layers need to receive that update together.
Do not assume that deleting an image file removes the associated candidate from every search index. The integration should expose indexing status and confirm that removals have taken effect. A background reindex that fails halfway must not make the portal look as though the whole catalogue is current.
Customers' uploads also need a defined lifecycle. A photo may include a serial number, a customer's facility or an employee's face in the background. Collect only what the workflow needs, restrict access and state how long uploads remain available. Avoid quietly turning an enquiry attachment into permanent model-training material.
Evaluate with the confusing items first
A pilot containing only clean catalogue photos tends to flatter the system. Build a test collection with worn items, poor lighting, partially hidden labels and visually similar incompatible products. Include objects the supplier does not sell. The ability to return no reliable match is part of quality.
Score the complete task, not just whether a correct product appears somewhere in the top results. Can the buyer distinguish the right variant? Does the portal ask the necessary clarification? Can a salesperson understand why the customer selected a candidate? These questions connect model evaluation to an actual purchasing process.
Keep a manual search and enquiry route available. A barcode or existing part number may resolve the task faster than an image. The best interface can use the most reliable evidence provided, rather than insisting that every customer experience the AI feature.
Scope a first release from photo to quote
Choose one product family with useful images and known variant rules. Connect candidate retrieval to the existing product information system, then carry the selected item and unresolved questions into the quotation workflow. Keep pricing and account-specific terms in the systems that already own them.
Ask vendors to separate catalogue cleanup, image processing, search evaluation and business integration in their proposals. A low model-inference estimate can obscure the larger job of making product information dependable. Require ownership of the mappings and evaluation fixtures so that changing the search provider does not require rebuilding the whole workflow.
Measure successful identification, avoidable clarification exchanges and quotes corrected because the suggested item was unsuitable. Establish the baseline before launch. More search interactions are not automatically a commercial improvement if they produce more incorrect enquiries.
Our guide to AI integration vendor evaluation helps structure the trial. The Netherlands API integration scope guide covers the responsibilities between systems. Through custom software development, TuniCyberLabs can connect the search experience to your catalogue and sales process. Send us a product family and the mistakes buyers currently make to define a useful visual-search pilot.