CapstonAI is an AI visibility platform that helps brands track, audit, and optimize their presence in AI-generated answers. It monitors how your brand is mentioned across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews, then provides actionable fixes to improve your share of voice in AI search results.

How does CapstonAI track AI visibility?

CapstonAI sends structured brand queries to multiple large language models (LLMs) on a recurring basis and records whether your brand is mentioned, cited, or recommended. It aggregates these results into a GEO score, a share-of-voice metric, and a citation report.

Which AI models does CapstonAI monitor?

CapstonAI currently monitors ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google), Perplexity, and Google AI Overviews. Coverage is continuously expanded as new AI search surfaces emerge.

CapstonAI offers a free WordPress plugin that provides basic AI visibility scanning for your site. For full AI visibility tracking, audit reports, automated content fixes, and multi-brand monitoring, the Core Plan is available at $99/month.

How is CapstonAI different from Semrush or Ahrefs?

Semrush and Ahrefs are built for traditional search engine optimization. CapstonAI is purpose-built for the AI search era: it tracks your brand presence inside AI-generated answers from ChatGPT, Claude, Gemini, and Perplexity.

Big Brand Bias In AI Search: Why Niche Brands Lose And How To Break Through (Toronto Cola Experiment)

Big Brand Bias in AI Search: Why Niche Brands Lose and How to Break Through (Toronto Cola Experiment)

The University of Toronto study by Chen et al. (arXiv:2509.08919, 2025) included a controlled “big brand bias” experiment in the cola vertical, contrasting major brands (Coca-Cola, Pepsi, Sprite, Dr Pepper, Fanta, etc.) against niche brands (Jones Soda, Faygo, Cheerwine, Zevia, Boylan, etc.) under unbranded prompts. The result: ChatGPT and Perplexity overwhelmingly defaulted to market leaders. Combined distribution across both engines: 62.2% major brands, 9.0% niche brands, 28.8% other. Coca-Cola and Pepsi alone dominated the head of the distribution. Below: the exact numbers, why the bias exists, and the 8-step playbook for niche brands to break through.

TL;DR: AI engines default to market leaders on unbranded prompts. Niche brands earn just 5-10% of citations on average. To break through, niche brands must: (1) target narrow verticals where they can dominate, (2) over-invest in earned media in specialty publications, (3) build YouTube and community presence (Perplexity-friendly), (4) earn Wikipedia notability where possible, (5) accept that ChatGPT and Claude are the hardest engines to crack, and (6) measure niche-brand citation share as a leading indicator.

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The exact numbers from the cola experiment

Chen et al. ran unbranded prompts (e.g., “Top 10 soft drinks”, “Best sodas”) through ChatGPT and Perplexity, classifying every mentioned brand as Major (20 leading brands including Coca-Cola, Pepsi, Sprite, Dr Pepper) or Niche (20 indie/craft sodas including Jones Soda, Faygo, Cheerwine, Boylan). The combined distribution:

Brand category	ChatGPT count	ChatGPT share	Perplexity count	Perplexity share	Combined share
Major brands	~250	~57%	339	67.9%	62.2%
Niche brands	~50	~12%	29	5.8%	9.0%
Other	~135	~31%	131	26.3%	28.8%

Brand-level head of distribution: Coca-Cola got 78 mentions on ChatGPT, 107 on Perplexity. Pepsi got 44 on ChatGPT, 58 on Perplexity. Dr Pepper consistently in the next tier (30, 34). Niche brands like Jones Soda, Boylan, Faygo, Sprecher surfaced occasionally but at much lower frequencies.

Why the bias exists (Chen et al. interpretation)

The Toronto study attributes the bias to two reinforcing mechanisms. First, evidence concentration: ChatGPT relied heavily on Wikipedia (~19.7% of cola citations) and a small set of mainstream consumer-review and food sites. Wikipedia is more likely to have detailed articles on major brands. Second, citation distribution: Perplexity distributed across a broader long tail (Others bucket ~60.6%), with YouTube the single most frequent domain (~8.9%), but still defaulted to market leaders despite the diversity. The conclusion: both engines marshal evidence differently, but under unbranded prompts both default to market leaders.

The 8-step niche-brand breakthrough playbook

Step 1: Pick your dominable niche. Trying to compete with Coca-Cola in “best soda” is a loss. Niche brands win in narrow categories: “best craft ginger ale”, “best low-sugar soda 2026”, “best regional sodas USA”. Define 5-10 narrow categories where your brand has a credible argument for top-3.
Step 2: Build category-specific earned media. Get coverage in niche-specific publications: craft beverage trade press, specialty food magazines, local/regional press for region-specific brands. The Toronto data shows AI engines reach into long-tail sources when prompted with specific categories.
Step 3: Invest heavily in YouTube content. Perplexity (the most niche-friendly major engine in the Toronto data) heavily cites YouTube. A reviewed-by-influencer YouTube video with strong watch time can be cited where it would never appear in Claude or ChatGPT outputs.
Step 4: Earn Wikipedia eligibility. If your brand has any notability (press coverage, longevity, cultural impact, distinct market position), pursue Wikipedia. Notability is the gate. Once in, Wikipedia is the highest-influence single domain in the Zhang et al. (2026) absorption analysis.
Step 5: Build comparison content positioning you against majors. Pages like “Coca-Cola vs Jones Soda: craft alternative comparison” exploit the Zhang et al. finding that comparison content is the highest-uplift evidence genre (+55.28% influence). Major brands rarely build these; niche brands should.
Step 6: Engage Reddit and community discussions strategically. Perplexity cites Reddit. Authentic engagement in niche subreddits (r/sodapop, r/craftbeer for analogous categories) builds the kind of community endorsement that compounds in Perplexity citations.
Step 7: Accept Claude and ChatGPT as hard markets. Both engines are conservative and authority-biased. Niche brands rarely break through quickly. Prioritize Perplexity, Gemini, and Google AI Overview while building the long-term Wikipedia and authority-publication coverage required for Claude/ChatGPT.
Step 8: Track niche-brand citation share as a leading KPI. If you measure only total citation share, you will despair: market leaders capture 60%+. Track your share of niche-brand citations (target: 25-40% within your defined narrow category) as the realistic metric for board reporting.

The strategic logic of niche-brand GEO

The Toronto cola data is brutal but useful. It tells niche brands: do not try to compete on unbranded category-level prompts. The structural bias is too strong. Instead, win on narrow, justified prompts where the buyer is already filtering for niche characteristics. “Best craft soda for cocktail mixing”, “best sugar-free root beer”, “best regional ginger ale” are queries where Jones Soda, Cheerwine, Zevia, Boylan have a real chance.

This is also where the Zhang, He & Yao (2026) evidence-container insight applies: comparison content, definition-rich content, and procedural how-to content rank highest for absorption. A page titled “Craft Ginger Ale Comparison: Brand-by-Brand Buying Guide” with definitions, comparison tables, and procedural buying advice is the kind of evidence container AI engines absorb deeply.

Common errors when fighting big brand bias

Pretending the bias doesn’t exist. Hoping that “great content” alone will overcome a 60-90% major-brand default ignores the structural mechanism. Niche brands must engineer narrow positioning explicitly.
Buying broad PR coverage. Generic press placements (“X soda brand to watch”) in non-vertical publications rarely move the needle. Niche-vertical specialty press has much higher GEO leverage.
Skipping YouTube. Perplexity is one of the only paths to fast niche-brand citation growth. YouTube content is the unlock.
Premature Wikipedia attempts. Wikipedia editors reject brand articles aggressively. Build genuine notability (sustained press coverage, third-party validation) before attempting.
Measuring against the wrong baseline. Niche brands comparing themselves to Coca-Cola in total citation share will quit. Compare to niche peers within the defined narrow category.

FAQ — Big brand bias in AI Search

Does the bias hold in B2B and services categories?

The Toronto cola experiment focused on CPG. The broader pattern (AI engines defaulting to known authority sources and major brands) holds across verticals. In B2B SaaS, “best CRM” prompts default to Salesforce, HubSpot. In services, “best CRM consulting” prompts default to McKinsey, Deloitte. The mechanism is the same: AI engines weight authority and frequency-of-mention, which favors incumbents.

Can a niche brand ever fully overcome the bias?

Yes, but only by becoming the de-facto authority in a narrow niche. Examples in CapstonAI partner cohort: Linear (project management) breaking through against Jira/Asana via developer community + design press coverage; Tailwind UI breaking through against Bootstrap via design publication coverage and community traction. The pattern: dominate the narrow vertical before attempting the broad category.

What’s the fastest path to break through on Perplexity specifically?

YouTube content with strong watch time, plus Reddit community presence in your vertical subreddits. Perplexity’s social-friendly retrieval makes these the highest-leverage tactics. Expect 60-90 days to see citation share movement on niche queries; longer for broader queries.

Tools and related reading

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Last updated: May 2026. Primary source: Chen, M., Wang, X., Chen, K., & Koudas, N. (2025). Generative Engine Optimization: How to Dominate AI Search. University of Toronto. arXiv:2509.08919. https://arxiv.org/abs/2509.08919