# AI Search Visibility Benchmark 2026: India & UAE

> Canonical HTML version: https://thejayant.in/blog/ai-search-visibility-benchmark
> Author: Jayant Solanki — https://thejayant.in/
> This Markdown file is a plain-text twin of the article at the URL above. Same content, no page furniture. It is public, not bot-only.

This page is the **study design**, not the findings. The results tables below are deliberately empty and every one is marked _pending_. Nothing here should be read, quoted or cited as a finding yet.

It is set to `noindex` until real data replaces the placeholders. The methodology is published first on purpose — pre-registering how you will measure, before you look, is what stops a study quietly becoming whatever the data needed to say.

## Why this study

There is a great deal of published advice about AI search visibility and remarkably little published measurement — particularly for the markets I work in. Almost every benchmark I can find is US-centric, and almost none of them publish their prompt set, which makes the results impossible to check or reproduce.

So the niche here is deliberate: **India and the UAE, commercial intent, open methodology.** Narrower than a global study, but genuinely uncontested, and far more useful to the businesses actually buying these services.

The hypotheses below come from [the thirteen factors that decide which sources AI engines cite](https://thejayant.in/blog/how-ai-chooses-sources) — this study exists to test them against a real market rather than restate them.

When a real buyer in India or the UAE asks an AI assistant a commercial question, **which sources get cited, and what do those sources have in common?** Not "how do I rank in ChatGPT" in the abstract — what is observably true across a fixed prompt set, tested identically on each platform.

## Methodology

### Platforms tested

| Platform | Surface | Mode |
| --- | --- | --- |
| ChatGPT | ChatGPT Search | Web search enabled, logged out, memory off |
| Google Gemini | Gemini app | Default model, fresh session |
| Perplexity | Perplexity Search | Default, not Pro/focus mode |
| Google Search | AI Overviews / AI Mode | Incognito, location set per market |

### Controls

Anything that lets results drift is a threat to the finding, so these are fixed in advance:

- **Logged out, personalisation off, memory disabled** — a personalised answer measures the account, not the web.
- **Fresh session per prompt** — no conversational context carrying over and biasing the next answer.
- **Location pinned per market** — India and UAE runs are separate, not averaged.
- **Fixed collection window** — all four platforms tested within the same 72 hours, since the web moves under you.
- **Verbatim prompts** — no rephrasing to "help" a platform along.
- **Screenshot every response** — the evidence, and the only defence against your own memory.

### Prompt set: 120 prompts, 6 verticals

Twenty prompts per vertical, split across three intent stages so the study can separate discovery from decision:

| Vertical | Discovery (8) | Comparison (6) | Decision (6) |
| --- | --- | --- | --- |
| SEO / digital marketing | "how do I improve my website's Google ranking" | "SEO agency vs freelance SEO consultant" | "best SEO consultant in Indore" |
| Website design & development | "how much does a business website cost in India" | "Webflow vs custom website for a small business" | "who can build my website in a week" |
| Local services | "how do I get my business on Google Maps" | "local SEO vs Google Ads for a service business" | "Google Business Profile expert in Dubai" |
| eCommerce | "why is my Shopify store not getting traffic" | "Shopify SEO vs WooCommerce SEO" | "eCommerce SEO agency for a 10,000 SKU catalogue" |
| SaaS | "how do SaaS companies get organic signups" | "content marketing vs paid ads for SaaS" | "SaaS SEO consultant India" |
| Marketing automation | "how do I automate marketing reporting" | "Make.com vs n8n vs Zapier" | "who can build a GA4 reporting automation" |

The full 120-prompt list ships with the results as a CSV, so anyone can reproduce or challenge the study. A benchmark whose inputs are secret is marketing, not research.

### What gets recorded per response

| Field | Values | Why it is measured |
| --- | --- | --- |
| Brands mentioned | List | Mention without a link still shapes the buyer |
| Sources cited | URLs | The actual citation, the thing being competed for |
| Domain type | Brand / Reddit / YouTube / publisher / directory / forum | Tests whether owned sites win at all, or aggregators dominate |
| Ranks in Google top 10? | Yes / No / Position | Tests how tightly citation tracks classic ranking |
| Content format | Guide / listicle / product / comparison / forum thread | Which formats get lifted |
| Freshness | Published / modified date | Whether recency correlates with citation |
| Entity signals | Org schema, sameAs, consistent NAP | Whether entity clarity correlates with citation |
| Word count | Integer | Depth vs brevity |
| Has original data? | Yes / No | Tests the information-gain hypothesis directly |
| Third-party mentions | Count | Off-site authority |

### Collection template

One row per prompt per platform — 480 rows at full coverage:

```
market,vertical,intent,prompt_id,prompt,platform,run_date,
brands_mentioned,sources_cited,domain_type,google_rank,
content_format,published_date,modified_date,has_org_schema,
has_sameas,nap_consistent,word_count,has_original_data,
third_party_mentions,screenshot_ref,notes

    <h3>Hypotheses, stated before looking</h3>
    <p>Written down in advance so they can be wrong:</p>
```

1. **H1** — Most cited sources already rank in Google's top 10 for a related query, supporting the view that AI retrieval sits on classic ranking.
2. **H2** — Aggregators, forums and directories out-cite brand-owned pages on discovery prompts, and lose ground on decision prompts.
3. **H3** — Pages with original data are cited disproportionately relative to how often they occur.
4. **H4** — India and UAE results diverge most on local-intent prompts and converge on informational ones.
5. **H5** — Overlap between platforms is lower than assumed; winning ChatGPT does not mean winning Perplexity.

Stated up front because a benchmark that hides them is not worth citing. Single-run sampling means model non-determinism is uncontrolled — the same prompt can return different sources. Results are a snapshot of one collection window, not a trend. 120 prompts across 6 verticals is a small n per vertical. Logged-out testing removes personalisation, which real users have. None of this invalidates the study; it bounds what it can claim.

## Results

## What this will let us say

Once populated, this becomes the thing almost no AI-search article currently has: **observed data from a stated market, with a published prompt set anyone can rerun.**

Whether it confirms the hypotheses or contradicts them is genuinely open — and a study that can only confirm what its author already sells is not a study. If H1 fails and cited pages routinely do not rank, that is a more interesting finding than if it holds.

## Reproducing it

When results publish, the prompt CSV, the scoring rubric and the aggregated dataset publish with them. Rerun it, disagree with it, or run it for your own market — the design is deliberately reusable.

While collection runs, the practical work does not stop: [what actually earns citations in AI search](https://thejayant.in/blog/how-to-get-cited-by-ai-search) is the checklist I would run today, and [a model with a hard knowledge cutoff](https://thejayant.in/blog/gpt-6-astra-seo-geo) shows why the timing of a study matters as much as its design.