I’ve watched three ecommerce brands run the same experiment this year. They each asked ChatGPT to name the best option in their category, and none of them liked the answer. Two weren’t mentioned at all. The third showed up with the wrong price and an old product line that got discontinued in 2023.
That’s the moment most teams start shopping for help. AI answers now sit above the blue links for a huge share of buying queries, and if the model describes your brand inaccurately, you’re paying for a sales pitch you never wrote. A generative engine optimization agency exists to fix exactly that gap, but only if the timing is right and you know what you’re buying.
Here’s the short version. Hire when AI answers are already shaping your category, when your own team can’t tell whether the problem is content or code, or when a wrong answer is costing you deals. Don’t hire when you haven’t fixed basic crawlability, or when nobody internally can approve content changes.
Why AI search changed the math for brands
Search didn’t disappear. It moved into a conversational layer where the model summarizes, compares, and recommends instead of handing over ten links. According to Census Bureau data on retail ecommerce, online sales have climbed for years, and more of that discovery now starts with a question typed into an assistant rather than a keyword typed into a search box.
What that means for you: your product page still matters, but so does every third-party page the model reads to decide what’s true about you. A review site from 2019. A Reddit thread. A distributor listing with stale specs. Those sources feed the answer whether you like it or not.
I’d argue this is the single biggest shift in product discovery since mobile checkout. Rankings rewarded pages. Answers reward consensus, and consensus is a much messier thing to manage.
When hiring a GEO agency actually makes sense
Not every brand needs outside help. Plenty just need someone on the content team to spend two weeks cleaning up product data and publishing comparison content. The agency case gets strong in five situations.
- You’re invisible in AI answers for your own branded queries. If the model can’t describe your flagship product correctly, the problem is foundational.
- You sell across many channels. Marketplace listings, retail partner pages, and your DTC site often contradict each other, and models notice.
- You have no way to measure it. You can’t tell if last month’s content push moved anything inside AI-generated responses.
- Your category is comparison-heavy. Supplements, electronics, home goods, and anything where buyers ask “which one” before “how much.”
- Leadership wants a number. Someone upstairs is asking what the AI strategy is, and you need a real answer with tracking behind it.
The flip side matters just as much. If your site still blocks AI crawlers by accident, if your product feed is a mess, or if you have no internal owner for content approvals, an agency will bill you for diagnosing problems you could have found yourself with a free audit tool and an afternoon.
How AI visibility gets assessed in the first place
This is where most agencies either earn trust or lose it in the first call. A serious assessment doesn’t start with tactics. It starts with capturing what AI systems currently say about you, across the platforms your buyers actually use, and then reverse engineering why.
The Baseline Trace is the exercise I run before any strategy work, and you should ask any candidate agency whether they do something like it. You build a fixed list of 30 to 50 real buyer questions, run them through multiple AI platforms on a set schedule, screenshot the answers, and log which sources the responses cite. Do it weekly for a month before you touch anything.
What you end up with is a map: which competitors get named, which pages feed the answers, and where your brand is missing. I’ve seen this process reveal that a single outdated review page was driving a competitor’s inclusion across a dozen queries. No amount of new blog content would have fixed it. The fix was getting that page updated, and it took one email.
That’s the texture of this work. It’s less about publishing volume and more about finding the handful of sources that shape what models believe.
Capabilities to evaluate before you sign anything
Plenty of agencies bolted “GEO” onto their service page in the last twelve months. Some are legit. Others are running the same SEO playbook with new vocabulary. Five capabilities separate the two.
1. Real SEO foundations, not SEO nostalgia
GEO doesn’t replace SEO. It builds on it. If a shop can’t explain how technical crawling, structured data, and internal linking work, they can’t help you get cited. Ask them to walk through your robots.txt and schema markup live on the call. The answer tells you plenty.
2. Technical depth beyond keyword research
Can they talk about how content gets parsed, how product feeds inform retail platforms, and what server-side rendering does to visibility? The National Institute of Standards and Technology publishes work on information quality and data trustworthiness that’s a reasonable litmus test for how seriously a team thinks about machine-readable accuracy. If the conversation stays at the meta description level, keep looking.
3. Product content strategy, not just articles
Models lean on structured product data, comparison pages, Q and A sections, and third-party validation. A real GEO plan touches PDP copy, spec tables, review management, and partner listings. Blog posts alone won’t move a comparison-era buyer.
4. A defined assessment process
Ask exactly what happens in week one. You want a documented baseline, a named set of tracked queries, and a stated measurement window. Vague answers about “building a strategy” mean they’re figuring it out on your dime.
5. Transparent measurement and honest limits
Nobody controls what ChatGPT says. Not you, not Blue Wheel, not anyone. Any agency promising guaranteed placement inside AI Overviews is either confused or lying, and the Federal Trade Commission has been fairly clear about how it feels regarding marketing claims a business can’t back up. What you can measure: share of answers where you appear, accuracy of the description, sentiment, and citations pointing to your domain. Ask how they report all four.
Questions to ask on the sales call
Bring these verbatim. The answers matter more than the deck.
- What exactly do I get in month one, and what’s the deliverable format?
- How do you track AI mentions, and can I see a sample report from another client?
- How long before we’d expect any movement, and what would make you say this isn’t working?
- Who does the work, and how much of it gets handed to a junior?
- What happens if the model stops citing us after an update?
- What’s the exit plan if we part ways in six months?
Realistic timelines, by the way: expect baseline work in the first 30 days, content and technical fixes in months two and three, and meaningful shifts in AI answers somewhere between month three and month six. Anyone quoting two weeks is selling hope.
Red flags worth walking away from
Guaranteed placement in AI Overviews. No baseline audit before strategy. Reports that count “content published” instead of visibility change. A single junior writer handling everything with no technical oversight. Pricing that’s suspiciously low because they’re really just running an old link-building package with new labels.
And the quieter one: an agency that never pushes back on your assumptions. Good partners tell you when your product data is the problem, even when you’d rather hear it’s a content gap.
Making the call
GEO isn’t a channel you switch on. It’s a maintenance practice, closer to reputation management than paid media, and it rewards teams who stay patient and specific. If your buyers ask AI tools which product to buy and your brand isn’t in the answer, that’s a gap worth closing, whether you handle it in-house or bring in help.
Start with the Baseline Trace this week, even if it’s just you and a spreadsheet. Once you can see what the models actually say, you’ll know within days whether you need to hire anyone at all. Which question in your category would hurt most if AI answered it wrong about you?
