Quick answer: We helped Threekit, an AI-native CPQ platform for manufacturers, increase its AI answer engine visibility from 17% to 25% and its owned domain citations from 48% to 65% in six weeks, using HubSpot’s AEO dashboard to identify content gaps and a weekly content and tracking process to close them.
HubSpot just published a case study about our client Threekit, and we want to tell you the part of the story we’re closest to: how the engagement actually started, and what it taught us about answer engine optimization for B2B companies.
First, the headline numbers, because they’re worth leading with. In six weeks, Threekit’s AEO brand visibility on key category terms climbed from 17% to 25%, and owned domain citations rose from 48% to 65%. For a company that had just repositioned into a new category with, in their VP of Marketing’s words, “basically zero visibility,” that’s real traction, fast.
Here’s how it happened from where we sat.
The problem nobody could see
Threekit had made a bold move. The company built its reputation on 3D product visualization, then repositioned around something bigger: an AI-native CPQ for manufacturers. Great product, clear market need, trillions of dollars of U.S. manufacturing already asking how to sell complex products faster.
But there was a gap between having the right product and being found by the buyers who needed it. Manufacturers evaluating CPQ software don’t start with a vendor call anymore. They open ChatGPT, Gemini, or Perplexity and ask which platforms are worth considering. The names those answer engines return make the shortlist. Everyone else is invisible.
In the category Threekit had just entered, they weren’t in the answers.
The tricky part is that this kind of invisibility doesn’t announce itself. Traffic reports don’t show you the conversations happening inside answer engines. Threekit’s team had tools for checking where the company appeared online, but nothing that showed ChatGPT, Gemini, and Perplexity side by side, benchmarked against competitors, on the prompts that actually mattered.
Our job was to make the problem concrete
As Threekit’s HubSpot partner, we brought the problem into focus. In one of our biweekly huddles this June, we shared HubSpot’s AEO dashboard with Marc Uible and his team, showed them exactly where Threekit stood in the category, and put a number on the gap.
The number was bad. But it was specific. And a specific number changes everything, because for the first time there was a baseline, a benchmark against named competitors, and a place to start.
Then we helped run the engine
Diagnosis was the start, not the whole engagement. Over the following months, our team worked alongside Threekit’s marketers week after week:
As part of our AEO service, we surfaced AEO recommendations weekly, flagging which prompts had moved, where competitors were gaining ground, and what content the data said to produce next. We provided suggested content prompts based on competitor and market analysis, then handled much of the publishing side: updating existing blog posts with new positioning and terminology, pruning thin content that was dragging down authority, and strengthening internal linking to the pages that mattered most for the category.
Threekit’s team owned what only they could own: the positioning, the messaging, the deep product expertise that made the content credible. Marc and his team moved fast and never let the data sit. That division of labor, their category knowledge plus our AEO operations, is what turned a dashboard into a weekly practice.
What happened next
The dashboard didn’t just measure the gap. It revealed what answer engines were looking for when buyers asked about the category: the exact language manufacturers were using, the comparison questions they were asking, the prompts where Threekit’s competitors were showing up and Threekit wasn’t.
Together, we turned those signals into a content plan. Blog posts, positioning pages, and thought leadership, each one written as a direct answer to a question the data surfaced. Then a weekly rhythm formed around it: check the dashboard, see which prompts moved, see which competitors gained ground, action the next content recommendations, track the change.
That last part matters more than any single piece of content. AEO went from a vague strategic priority to a repeatable process with measurable outputs.
Three things this engagement taught us
- AEO invisibility is a silent pipeline problem. If answer engines don’t recommend you, you don’t lose deals. You lose the chance to be considered at all. There’s no lost-opportunity report for the buyer who never found you. The only way to know is to measure.
- The diagnosis is the unlock. Threekit’s team is talented and moves fast. What they were missing wasn’t capability, it was a clear picture of where they stood. Once the gap had a number attached, execution followed quickly. In our experience, this is the most common pattern: companies don’t fail at AEO because they can’t write content. They fail because they’re guessing at what to write.
- Speed beats perfection during a repositioning. Threekit didn’t run a multi-month strategy project before acting. They got a baseline, got a prioritized list of content the data said to produce, and started shipping. Six weeks later the numbers had moved. The window for defining a new category doesn’t stay open while you deliberate.
The bigger picture
Every time an answer engine recommends Threekit to a manufacturer, that buyer arrives at the first sales conversation already understanding what the platform does. That’s what upstream visibility buys you: conversations that start further along.
This is the work we care most about at Simple Machines: closing the gap between knowing AI is reshaping how buyers find you and actually doing something about it. Threekit is what that looks like when it works.
The partnership runs in both directions, too. Threekit recently wrote about why AI-native CPQ depends on trustworthy CRM data, and why they point manufacturers to our Data Trust practice to build that foundation. Their take, in short: the smarter the quoting tool, the more it depends on the data underneath it.
You can read HubSpot’s full case study here.
Wondering where your brand stands when buyers ask ChatGPT about your category? Let’s find out. Our AEO practice starts with the same diagnostic we ran for Threekit.
