When Luma Nutrition asked us to improve their AI-search visibility, the goal was specific: get recommended by name when a buyer queries "best magnesium glycinate supplement" inside ChatGPT, Perplexity, Claude, and Gemini. Not "mentioned somewhere in the response" — recommended. By brand. In the shortlist.
The timeline was five weeks. Here's the sequence we ran.
Why AI citations are different from Google rankings
Ranking for a query on Google means owning a specific URL position. Being cited inside an AI answer means something different: the language model has to have learned, during training or retrieval, that your brand is a canonical option for that category. That's a different game — and it's won through entity signals, not just backlinks.
Three categories of signal matter:
- Structured sources the models pull from during answer synthesis — Wikipedia, authoritative listicles, structured product databases, retailer pages with clean schema.
- Training-data footprint — what the model "knows" from its most recent training cut. Reddit, Quora, and community forums are heavily weighted here because they contain unpaid brand mentions at high volume.
- Real-time retrieval — Perplexity and ChatGPT Search hit live web results. Classic SEO signals feed these, but with paragraph-level granularity.
A good AI SEO program touches all three in parallel. That's what we ran.
Week 1–2: Authoritative listicle placements
The fastest lever for most AI engines is editorial listicle placement. When a user asks "best magnesium glycinate," models tend to synthesize their answer from authoritative "best X" roundups that they've already indexed.
We placed Luma Nutrition into three listicle features during weeks 1–2: a health publication roundup of glycinate brands, a nutrition-focused product comparison, and a third-party review hub with category-specific benchmarks. Each placement included specific language we wanted the model to learn — ingredient origin, CFU count analogs, and the bioavailability story.
Week 3: Reddit and Quora seeding
Reddit is the single largest non-Wikipedia data source for most open-weight and closed frontier LLMs. If your brand isn't discussed organically there, the models effectively don't know about it — and will default to whichever competitor is mentioned.
We ran a compliant Reddit program: genuine engagement in r/Supplements, r/Nootropics, and a few smaller communities where glycinate is actively discussed. Real accounts, real opinions, real disclosure. Luma mentioned in the context of specific use cases — not spammed, not coordinated, not astroturfed.
On Quora, we answered four high-volume questions with detailed, educational responses that organically referenced Luma alongside three competitors. By week 3 end, Luma was appearing in Reddit threads with 2–4 mentions per high-engagement post on target subtopics.
Week 4: Wikipedia and entity consolidation
Wikipedia isn't where most brands should focus first (it's hostile to promotional edits and requires independent notability) but for a brand already mentioned in independent publications, it's a high-leverage signal.
We didn't edit Luma's Wikipedia entry directly (that would fail notability review). Instead, we focused on entity consolidation across Wikidata, Google's Knowledge Graph, schema.org Brand and Product markup on Luma's own pages, and structured data on third-party retailer and review sites.
The goal: when any AI engine does an entity lookup for "Luma Nutrition magnesium glycinate," it gets clean, consistent, cross-referenced metadata from at least five independent sources. That's what makes a brand feel "real" to a model's internal representation.
Week 5: PR push and schema for real-time retrieval
For Perplexity and ChatGPT Search — which index the web in near-real-time — we shipped two PR placements in nutrition publications with aggressive FAQ schema embedded. Each FAQ answer was written to match the exact phrasing of the target buyer query.
This is where AEO meets classic SEO. The models retrieve, rank, and synthesize paragraphs from freshly-indexed pages. If your paragraph is the cleanest, most-cited answer to the query, you win.
The results at week 5
By the end of week 5, "best magnesium glycinate supplement" returned Luma in the recommended shortlist on all four engines:
- ChatGPT — cited Luma by name in 4 of 5 test queries across variations.
- Perplexity — included Luma in the first response for 6 of 7 variants tested.
- Claude — recommended Luma in 5 of 5 tests.
- Gemini — included Luma in its answer for 4 of 6 queries (Gemini tracked closest to Google and had the longest lag).
Referral traffic from ChatGPT and Perplexity began appearing in analytics by week 6. More importantly, branded search volume for "Luma Nutrition magnesium" lifted 180% month-over-month — the tell-tale sign that users were querying AI first, then Googling the brand name.
Three takeaways for AI SEO in 2026
First: AI citations compound. The more a model sees your brand in its training and retrieval pipelines, the more confidently it recommends you. Week 5 was when we hit all four engines — but the work continues to solidify placement over the following quarter.
Second: Reddit matters more than most brands realize. If you're invisible there, the models don't know you — and they default to your competitor. Reddit + Quora + niche forums is the single most cost-effective AEO lever we've found.
Third: The win isn't just traffic. It's branded search lift. AI SEO is a top-of-funnel channel that feeds your classic SEO and direct-brand search — and that second-order lift often outsizes the direct AI-referred clicks.
If you want us to run this sequence on your brand, book a free AI SEO audit — we'll show you where your brand currently sits across the four major engines today.