Start with the honest part. There are zero standalone in-house postings for this title — we searched, and the result set is empty (verified). That isn’t a gap to apologize for. It’s an open lane. The duties are real and they get paid for. They just sit buried inside GEO and AEO roles, which makes this the cheapest way into the field. So treat it as a function, not a title: you run the prompt panel that shows a distributor what the answer engines say about its products today, who they cite, where it’s invisible, and where the answer is wrong about a pressure rating or a part number. The chapters below walk the work, from running the monitoring to handing the optimizer a fix list.
Why this isn’t a job title yet
The tool category is funded and real. Profound, Peec, Otterly, Scrunch, and Ahrefs Brand Radar all sell into it. The duties show up embedded in other roles — Citizens Bank’s AEO Manager posting even names Brandlight as tooling. So the function exists and gets paid for. The title just hasn’t crystallized. Don’t wait for it to.
What you do is narrow and concrete. You watch what the answer engines say about a distributor, on a schedule, against named competitors, and you flag what’s wrong. The artifact is the monitoring report, and the report is the thing that makes a buyer who never thought about AI search suddenly care. 51% of B2B buyers now start research in AI chatbots (G2, Apr 2025). The report shows a distributor exactly where it stands in those answers. Usually that’s nowhere.
Build the prompt panel
The prompt panel is the core deliverable. A fixed set of real buyer questions, run on a schedule so you can see the line move over time. In industrial the questions are already question-shaped and part-number-shaped, so you don’t invent them. You collect what buyers actually type.
In hydraulics that’s “Gates equivalent of Parker 387 hose,” “seal kit for a Char-Lynn 104 motor,” “NPT vs JIC vs ORFS,” “best hydraulic hose supplier for ag OEMs.” In automation it’s cross-reference and obsolescence: “1756-L61 replacement,” “what replaces the discontinued PowerFlex 4?,” “SLC 500 to CompactLogix migration.” In broadline MRO, procurement already prompts for supplier discovery — “food-grade vs H1/H2 lubricants” — and you watch whether an independent house ever surfaces where Grainger’s product-listing pages never win. Around 20 prompts is a sane starting default, not a magic number. Pick the questions that matter. Skip the round count.
Track mentions, citations, and share of voice
Three things get measured, and they aren’t the same thing. Mention rate is how often you come up at all. Citation share is how often you’re the named source the engine links to. AI share of voice is your slice of the answers against named competitors. A distributor can get mentioned a lot and cited never. That gap is the story.
Score it against specific rivals, not the whole market. Pick two named competitors and measure your citation share against them across the panel. Every tool reports a different slice of this: Profound and Peec lean enterprise, Otterly is the cheap entry point, Scrunch and Ahrefs Brand Radar add their own angles. Know what each one actually counts before you quote its number to anyone. They don’t all mean the same thing by “visibility.”
Catch the hallucinations
This is where industrial monitoring earns its keep. In most categories a wrong AI answer is embarrassing. In hydraulics, automation, and safety gear it costs a customer. A wrong pressure rating. An interchange stated backwards (SKF to NTN bearing, the wrong way round). An ANSI/ISEA cut level off by a tier. A discontinued breaker listed as current. Automation cross-references are prime territory: the real answers live in gated OEM PDFs, so the engine guesses, and the guess sounds confident.
Log every wrong or misattributed answer, with the prompt, the engine, and the correct fact beside it. That log does two jobs at once. It’s a safety net for the buyer’s customers, and it’s the raw material for the correction work the GEO team does next. Accuracy governance isn’t a one-off audit. It’s a repair loop you run as long as the engines keep getting it wrong.
Turn the report into the GEO case
The monitoring report is the wake-up call. It’s also where the analyst function stops reporting and starts diagnosing. A buyer doesn’t need a dashboard. They need to know why a competitor wins the answer and they don’t, and what to do about it. So you take the worst result and explain it in one line. Is the competitor cited because their interchange chart is crawlable HTML while yours is a PDF? Is there an entity gap, the engine unsure what you even sell? Is the retrieval crawler blocked at the WAF?
That diagnosis becomes a prioritized fix list the GEO or optimizer work executes. Monitoring doesn’t replace the fix. It justifies it and measures whether it worked. Hand off the list, keep running the panel, and show the buyer the share-of-voice line move. That loop is the whole pitch. Don’t overclaim attribution to make it land.