How electronic components buyers search now — and why your catalog isn't the answer

An engineer looking for a substitute for an obsolete MOSFET doesn't start with your part search box anymore. He opens ChatGPT or Perplexity and describes the job: package, voltage rating, RDS(on), lead-free status, in-stock now. If your catalog can't answer that in a format a model can extract cleanly, your part never enters the answer — a competitor's line item does. That's the new filter electronic components distributors are being run through, and most catalogs were built for an ERP export, not a language model.

Buying research has moved into the chat window

This isn't a hypothetical for component buyers specifically, it's a documented shift across B2B purchasing generally. A recent industry analysis found 94% of B2B buyers used AI somewhere in their most recent purchase process, up from 89% the year before, with AI now cited as buyers' single most meaningful research source, ahead of vendor websites, sales reps, and product experts (Machine Relations, B2B Buyers Now Research Vendors in AI Engines). Within that research, 55% of buyers say they compare vendors directly inside AI tools and 54% use AI to research the product itself, often before a distributor's sales team even knows the deal exists.

Component sourcing has its own version of this problem, and it predates chatbots. Sites like Octopart already built a business on aggregating datasheets, parametric specs, lifecycle status, and pricing from hundreds of distributors and thousands of manufacturers into one machine-searchable index covering tens of millions of parts (Octopart, About). Searching by parameters and cross-references instead of a fixed part number is exactly what buyers now expect an AI answer engine to do conversationally, and the model doesn't page through ten distributor sites to find the one with a clean spec table — it picks whichever source it can parse fastest and cites that.

There's a visibility cost here beyond one lost RFQ. A Moz analysis cited in that same research found 88% of citations inside Google's AI Mode come from pages that don't even appear in the organic top 10 (Machine Relations) — ranking well in classic search no longer guarantees you show up in the answer, and it's a separate competition that runs on how legible your product data is, not how much of it you have.

Why a full parts catalog looks empty to an LLM

Most electronic components data was built for a counter clerk keying part numbers into an ERP, not for a model deciding whether to recommend you. A typical feed row looks like this:

Raw ERP feed description (as-is):

RES 10K 1% 1/8W 0805 SMD RoHS

A procurement engineer who already knows the part family can decode that in a glance. A language model trying to decide whether this resistor is a valid substitute for the one specified in a customer's BOM has almost nothing solid to anchor on: no confirmed manufacturer, no MPN, no temperature coefficient, no verified lifecycle status, no cross-reference to the part it's meant to replace. Component data is also unusually prone to drift — the same physical part sells under different distributor SKUs, manufacturers issue running changes under the same base part number, and lifecycle status (Active, NRND, EOL) changes on a schedule the ERP feed was never built to track in real time.

Faced with that ambiguity, an answer engine does the conservative thing: it skips the part, or hedges the citation so heavily it isn't really a recommendation.

What machine-readable component data looks like

Enriched, the same line reads like this:

That table isn't formatting for its own sake. It's the raw material for Product and ProductModel structured data, and it maps directly onto the identifiers — mpn, GTIN, package, and spec properties — that Schema.org already defines for exactly this kind of part-level detail (Semaku, Is Schema.org a game changer for the electronic component industry?). It also mirrors the standardized data fields the industry's own trade groups have been pushing distributors and suppliers to exchange for years, for the same reason: so systems on both sides can trust the field without a human double-checking it (ECIA specifications, EIGP 114.1).

Ask an answer engine: "What's an in-stock, RoHS-compliant 10 kOhm 0805 resistor, 1% tolerance, that's a verified substitute for a Vishay CRCW0805 that just went end-of-life, and who has it in stock?"

If your catalog carries verified resistance, tolerance, package, compliance, and lifecycle status as structured attributes, an answer engine can match that query and cite your listing with a specific MPN. If that same information lives only in an abbreviated string only a buyer who already knows the part can read, the model has nothing defensible to point to, and it moves on.

The gap is a data problem, not a content problem

Distributors in this space rarely lack part coverage. They lack part data structured well enough for a model to trust on the first pass. A full PIM migration can eventually fix that, but it's a multi-year project most won't greenlight to solve a search-visibility problem. The more direct fix is treating enrichment as its own layer: pull the feed as-is — even from a flat file — verify attributes against manufacturer datasheets, score each field for confidence, and push the result back out to the site and the PIM, without ripping out the ERP already in place.

Where this is heading

The distributors who get cited in AI answers over the next few years won't be the ones with the deepest part libraries. They'll be the ones whose data a model can parse in one pass and trust enough to recommend by name. That's the same discipline product-data enrichment has always been about — not a new content strategy, just the existing job of making a catalog legible, now graded by a stricter reader. Anglera's enrichment layer plugs into whatever a distributor already runs, or nothing at all, and turns thin ERP-style rows into verified, structured product data in weeks rather than years, so that legibility is a byproduct of how the catalog is maintained.