How AI Search Visibility Works — and Why Brand Wins Local

Proximity used to be the whole game in local search. Be close, be verified, be consistent — and the local pack rewarded you. That calculus hasn’t disappeared, but a new variable has entered the equation: whether an AI knows your brand exists at all.

What 17 Months of ChatGPT Traffic Data Actually Tells Us

Semrush’s analysis of 17 months of ChatGPT clickstream data reveals a pattern that should reframe how local and regional marketers think about discoverability. ChatGPT is generating meaningful referral traffic — but it’s not distributing that traffic evenly. Brands that appear frequently in AI-generated responses capture a disproportionate share of clicks, while lesser-known players get filtered out before the conversation even begins.

For Southeast Asian markets, the implication is sharper. Mobile-first users in Manila, Jakarta, or Bangkok are already adopting AI chat interfaces as a first-stop research tool — especially for service queries like “best dermatology clinic near Sukhumvit” or “reliable logistics provider in Cebu.” If your brand isn’t part of the AI’s working knowledge, you’re invisible at the moment of highest intent. Proximity to the user means nothing if the model doesn’t know you’re there.

The strategic response isn’t to chase AI optimisation as a separate workstream. It’s to recognise that the same brand signals that feed Google’s local pack — consistent NAP data, authoritative mentions, structured content — are exactly what LLMs index when forming responses.

The Moz Brand Bias Experiment Changes the Local Visibility Conversation

Dr. Peter J. Meyers at Moz ran 300 prompts through multiple LLMs to measure how often brand names surface in AI responses — and the findings are worth sitting with. Brand-forward queries generated significantly more named responses than non-brand queries, with Gemini showing a particularly pronounced tendency to reference established brand names even in ostensibly neutral prompts.

For local SEO practitioners, this isn’t just an academic curiosity. It’s a signal that brand equity — the kind built through earned media, review volume, and consistent digital presence — now functions as a soft ranking factor inside AI systems. A mid-sized F&B chain in Ho Chi Minh City with 400 Google reviews and regular mentions in local food media is more likely to surface in an AI response than a newer competitor with a better-optimised website but thin brand footprint.

The practical implication: local brand-building and search optimisation are no longer parallel tracks. They’re the same track. Campaigns that generate press mentions, community engagement, or third-party review volume are simultaneously building AI visibility — even if that was never the brief.

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Why “Soft-Brand” Queries Are the Local SEO Opportunity Nobody’s Optimising For

The Moz data introduces a useful concept: soft-brand queries — searches that reference a brand category or reputation attribute without naming a specific brand (think “most trusted mortgage broker in Chiang Mai” or “sustainable fashion brand Jakarta”). These queries sit between branded and non-branded intent, and LLMs handle them differently than traditional search engines do.

In conventional local SEO, these queries are won through keyword-optimised landing pages and review schema. In AI-generated responses, they’re won through the accumulated weight of how a brand is described across the web. If the language used in your reviews, press coverage, and social mentions consistently reinforces a specific attribute — “fastest delivery,” “most transparent pricing,” “locally sourced” — that signal becomes part of how an LLM characterises your brand in response to soft-brand queries.

For teams managing multilingual markets across Southeast Asia, this raises an implementation challenge: the attribute signals need to be consistent across Bahasa Indonesia, Thai, Tagalog, and English simultaneously. A brand celebrated for reliability in English-language reviews but described inconsistently in local-language content will generate weaker AI visibility than one with coherent brand language across all channels.

What This Means for Local Search Strategy in 2026

The temptation is to treat AI search optimisation as a new discipline requiring new tools. The more accurate read is that it rewards the fundamentals — done with greater intentionality than most brands have applied before.

Three operational shifts are worth prioritising now. First, audit your brand’s descriptive language across all third-party platforms — reviews, directories, earned media — and identify whether a consistent set of attributes is emerging or whether the signal is noisy. Second, build a content strategy that explicitly addresses soft-brand query categories in your category and geography — not just “best [product] in [city]” but the specific trust and value attributes your target customers use when they don’t yet have a brand in mind. Third, treat your Google Business Profile as an AI data source, not just a map pin — the structured information, Q&A content, and review responses you publish there are increasingly part of what LLMs consume when forming local recommendations.

The brands that will own local AI visibility in Southeast Asia aren’t the ones scrambling to reverse-engineer LLM prompts. They’re the ones that have made themselves undeniably, consistently, specifically known — in the right language, on the right platforms, with the right proof points.

The question worth sitting with: if an AI were asked right now to recommend the top three brands in your category and city, what would it say — and how much of that answer do you actually control?

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At grzzly, we work with growth teams across Southeast Asia on exactly this intersection — building the local search foundations that feed both traditional rankings and AI visibility. If you’re navigating how brand strategy and search intelligence connect in your market, Let’s talk.