Getting Cited by AI: SEO & GEO Lessons from Resilient Tec

A two person Shopify store can get named by AI assistants next to suppliers many times its size, if it answers the questions buyers are actually asking better than anyone else. That is the main thing I learned using Claude to promote Resilient Tec in 2026.

Resilient Tec is a small IT hardware reseller in Knoxville, Tennessee. I started it in 2026 after 17 years in the cable and networking business. We sell DAC cables, optical transceivers, structured cabling, and refurbished GPU servers through this store, eBay, and direct sales. I don't have a marketing team. Claude has been my research partner, writer, and Shopify operator for listings and content.

This post is for other e-commerce owners who want to do the same. Two terms come up throughout:

• SEO (search engine optimization) is getting your pages to rank in Google and Bing results.
• GEO (generative engine optimization, also called AEO) is getting AI assistants like ChatGPT, Gemini, Copilot, Perplexity, and Claude to cite and recommend you in their answers.

Part 1 covers the lessons that matter most. Part 2 is the playbook: the filter I use to pick products, the pipeline, the post template, the technical checklist, and the prompts I use with Claude. I've included real product names, part numbers, and results, including what hasn't worked.

Results so far

The wins are real but uneven: AI assistants now cite our content heavily, and the first content-driven sales have arrived, but plain Google rankings and blog-to-sale conversion are still works in progress.

What worked

What hasn't moved yet

• Google rankings don't hold on their own. The MMA4Z00-NS400 pricing guide dropped back out of Google results by mid-October, two weeks after reaching page 1.
• Traffic isn't sales. Blog posts get daily visits, but few readers buy. Adding product links to every post (Lesson 5) was the response.
• Competitors still outrank us on some terms. Petronella Tech outranks us on Google for "how to connect DGX Sparks." Perplexity still leaves us off some DGX Spark cable answers.
• Reviews are thin. Customers rarely leave reviews, and review count is a trust signal both Google and AI shopping tools use.

Part 1: The lessons

The biggest shift was moving from "rank for keywords" to "be the best answer to a buyer's question, with a price and a link attached." Everything else supports that.

1. Find the confusing question, not the keyword. Our breakout product was an interconnect cable for the NVIDIA DGX Spark, a popular desktop AI computer. Buyers couldn't tell from NVIDIA's documentation which cable they needed. We answered that clearly, with the part number and a buy link. Look for products where buyers are confused and few sellers bother to explain.
2. Write the answer first. AI assistants lift a passage, not a whole page. Open every post with a direct two- or three-sentence answer. Use headings phrased as the questions people type, and end with an FAQ.
3. Accuracy is the moat. One wrong compatibility claim costs more trust than ten good posts earn. We keep a ground-truth terminology document for each product line and correct published posts when we learn more. One example: we found that three DGX Sparks can connect without a switch, so we fixed every post that said two was the limit.
4. Put the price and "buy here" inside the answer. AI tools cited our DGX Spark posts but never said what the cable cost or that you could buy it from us. Separately, Gemini left us out of a transceiver price comparison and wrongly called us "quote-based." We now state the price, the seller, and a direct product link in the answer paragraph, the FAQ, and the page's structured data.
5. Give every post a path to the cart. Our Aruba vs. Cisco post earned about 400 Copilot citations and almost no sales. It ended with a bare call to action, and we had no Aruba switches in stock. Every commercial post now ends with a "Ready to Buy?" product block and a "You Might Also Need" cross-sell block.
6. Write for machines and humans at the same time. Structured data (JSON-LD), product feeds, and search-engine registrations are invisible to readers but are how AI tools read your store. AI assistants find you two ways: by reading your live pages and by reading your product feed. You need both working.
7. Each AI assistant has its own front door. Copilot answers from Bing's index, Gemini from Google's, and Perplexity and ChatGPT lean more on community sites like Reddit. Being strong in one doesn't make you visible in the others.
8. Build clusters, not one-off posts. A product page plus three to five posts that answer related questions, all linked to each other and to a hub page, outperforms a single post. Our part-number crosswalk page and DGX Spark interconnect reference page do this.
9. Protect what's working. Once a page earns rankings or citations, don't change its URL, and don't sweep it into bulk edits. Add to it instead of rewriting it.
10. Expect a lag, then volatility. Our MMA4Z00-NS400 pricing guide showed nothing for three weeks, hit page 1, then dropped out two weeks later. Track the same searches weekly so you see the trend, not one snapshot.
11. Stay the editor. Claude drafts, researches, and executes. I approve anything that goes live on the store. The best results came from pushing back on Claude's first answer when something looked off.

Part 2: The playbook

Step 1: Pick products with the filter

The products that win in AI answers pass four tests. Our DGX Spark cable passed all four, so we turned the pattern into a project called Parts Gap and hunt for more like it.

1. A hot platform. Lots of people are buying something new (a DGX Spark, a ConnectX-7 card, an HPE GPU server).
2. A required, inexpensive add-on. The platform doesn't work the way buyers want without a specific cable, optic, or kit.
3. Confusing official documentation. Part numbers changed, two lookalike parts exist, or the manufacturer's pages contradict each other.
4. A thin field of sellers who explain. Plenty of stores list the part. Few answer the question with a link attached.

We also drop opportunities that fail a cost or demand check. Claude's research ruled out co-packaged optics and linear pluggable optics as stocking plays, for example. For older server parts, we target the second and third most recent generations: new enough to have buyers, old enough that the big sellers have moved on.

Examples we've run through the filter

Step 2: Run every product through the same five stages

Finding the opportunity was never my bottleneck. The production work after it was. Turning it into a fixed five-stage pipeline cut the time each product takes and keeps quality consistent.

1. Intake. Confirm the supplier, cost, lead time, warranty, and the exact part numbers (OEM, cross-reference, and our own). Write a ground-truth sheet that every later stage must follow.
2. Question research. Have Claude find the real questions buyers ask about this part on manufacturer forums, Reddit, and in AI answers. Note which competitors answer them and what they get wrong.
3. Listing copy. Write the product page: a title with the part number and the platform it serves, a compatibility table, an FAQ, and complete Product schema. Add to existing listings rather than rewriting them.
4. Blog cluster. Write three to five posts that each answer one of those questions. All of them link to the listing, to each other, and to a hub page.
5. GEO structuring and publish. Apply the post template below, check schema and prices against the live store, and publish only after I approve.

Two variations have worked well:

• Big-ticket items get a two-post pair. For the Dell R740xd GPU server, one post answers "what else do I need to make this work" and the other answers "what will it actually do." They link to each other and both end at the listing.
• An editorial lane builds credibility. Some posts exist only to show we follow the industry, such as an explainer on NVIDIA's NVLink 6. These get the same structure and schema but no product blocks.

Step 3: Build every post from one template

Every commercial post on our blog follows the same structure, top to bottom. Claude applies it automatically because it's written into the project instructions.

1. Title phrased as the buyer's question, with the part number in it. Example: "Is a Third-Party MMA4Z00-NS or MMA4Z00-NS400 Safe?"
2. Answer-first opening. Two or three sentences that fully answer the title, including our price and a direct link to the product.
3. Question-shaped headings for each section, matching how people phrase follow-up questions.
4. A comparison or compatibility table where one makes sense (for example, all eight GB10 systems and which cable each needs).
5. An FAQ section of five to eight short questions and answers.
6. "You Might Also Need" linking related products a buyer in this situation also needs.
7. "Ready to Buy?" with the product, price, and stock status ("in stock at Resilient Tec" rather than a count that goes stale).
8. Sources linking the manufacturer documents and forum threads the post relies on.
9. Hidden structured data: BlogPosting and FAQPage schema, plus complete Product/Offer schema with image, brand, part number, availability, and a price that matches the live store.
10. SEO title and meta description set separately, with the price in the description where it fits.

Step 4: Fix the technical plumbing

Good content does nothing if search engines and AI tools can't read your store. These are the items Claude helped me find and fix. Most take under an hour each.

Product feeds

• Connect Google Merchant Center through Shopify's Google & YouTube channel, with shipping and return policies set for every country you ship to.
• Connect the Microsoft channel in Shopify too. Ours wasn't connected at all, which is why we saw no Bing traffic.
• Check every product's condition. Our new cables were being flagged "Used" in structured data.
• Turn on Shopify's Agentic Storefronts channel (ChatGPT, Copilot, Gemini and others). Verify AI-sourced orders against each order's customer journey rather than trusting the channel report.

Search engine accounts

• Verify the site in Google Search Console and fix what it flags. It caught Product markup in our posts that was missing an image.
• Verify the site in Bing Webmaster Tools (it imports from Search Console), submit the sitemap, and set up IndexNow so Bing sees new posts in days, not weeks.
• Watch Bing's AI Performance report. It shows which pages Copilot cites and for which questions.

Structured data

• Add FAQPage schema to posts. Shopify generates basic article schema but not FAQ schema.
• Keep product schema descriptions under Google's 5,000-character limit.
• State shipping terms in plain text on the site, not only in checkout. Ours: free US shipping, no minimum.

Trust signals

• Turn on Google Customer Reviews on the order confirmation page.
• Claim Google Business Profile, Bing Places, LinkedIn, and industry directories, with your business name, address, and phone identical everywhere.
• Answer questions in the communities your buyers use. For us that's r/homelab, r/LocalLLaMA, and the ServeTheHome forums.

Step 5: Set Claude up to work like a teammate

Claude got much more useful once it had standing context and access to the store, instead of starting from scratch each conversation.

Setup that made the difference

• A Claude Project per workflow. Ours include a listing intake project and a blog project. Each has written instructions (the post template, the pipeline, our rules) plus reference files: the part-number library, a finished listing as a format example, and the ground-truth sheets.
• The Shopify connector. Claude can read products, posts, and orders, and make edits after I approve them. It found problems I didn't know about, like unpublished products and the missing Microsoft channel.
• Memory. Claude remembers my standing rules (draft before publishing, don't touch DGX Spark listings in bulk, don't cite certain competitors) so I don't repeat them.
• Scheduled tasks. A weekly task checks whether we appear for our target searches and logs the result, and another checks that blog prices match the store.

Working rules

• Claude drafts; I approve before anything goes live.
• Changes are additive. No rewriting a page that already ranks or sells.
• On a new batch, stop after the first product and review it before Claude does the rest. This catches format drift early.
• Ask Claude where a claim came from. If it can't point to a source, it doesn't go in.

Prompts that worked

Mistakes worth avoiding

Most of our setbacks came from details, not strategy. Each of these cost us time or sales.

• Writing about products we didn't stock. The Aruba vs. Cisco post sent readers looking for Aruba switches we didn't have. Check inventory before you publish a comparison.
• Hard-coding numbers that change. Prices and stock counts written into posts went stale. Now posts say "in stock at Resilient Tec," and a weekly check compares post prices to the store.
• Listing one product twice. The same DGX Spark stacking cable ended up on two listings with separate stock counts, which risked overselling. Decide on one listing or shared inventory up front.
• Letting a wrong claim stand. We published that DGX Sparks could only connect two at a time without a switch, then learned three works. AI tools repeat what you publish, so correct it everywhere the moment you know.
• Assuming published means found. Our MMA4Z00-NS400 posts weren't indexed by Google for three weeks. Check Search Console after publishing instead of waiting for sales.
• Expecting content to fix price. A competitor listed the same QSFP112 cable at about half our price ($95 vs. $187). Visibility gets you considered; price still decides the sale.

Start this week

You don't need the whole system to see results. These five steps are where I'd start again.

1. Ask ChatGPT, Gemini, Copilot, and Perplexity the questions your buyers ask. Write down who they name and whether you appear.
2. Pick one product where buyers are confused, and have Claude write a ground-truth sheet for it.
3. Rewrite that product's page and one post with the answer first, your price and a buy link in the answer, and an FAQ.
4. Connect both the Google and Microsoft channels in your store, and verify the site in Search Console and Bing Webmaster Tools.
5. Set up a weekly check on the same three searches, and give it a month before judging.

Questions about any of this? Get in touch with us.