How Four AI Platforms Recommend IVF Clinics in Greater Noida West: A 25-Query Study

Most doctors in India check their Google ranking. Almost none check their AI ranking.

That gap is closing fast. As we documented in our earlier post — We Tested AI Search Across 4 Engines: How Patients Find Doctors in India — patients are increasingly using ChatGPT, Gemini, Perplexity and Google AI Mode as their first stop when looking for a specialist. AI doesn't show a list of links. It makes a recommendation. And the clinics that get recommended are not always the ones that rank highest on Google.

That first post covered the general principles. This one goes deeper — with actual query data.

In May and June 2026, DoubleSure ran a structured audit of 25 healthcare search queries specifically for the IVF and fertility specialty in Greater Noida West. We tested across all four major AI engines, captured screenshots of every result, and documented which clinics and doctors appeared, where they appeared, and why — to the extent the results made it apparent.

What follows is the full published output of that audit.

1. Research Methodology

What we tested

We designed 25 queries across 7 intent clusters specifically representing how patients in Greater Noida West search for IVF and fertility care. The intent clusters were:

• General IVF / local discovery — broad "best IVF clinic near me" searches
• Hyperlocal proximity — searches anchored to specific localities (Gaur City 2, Bisrakh, Noida Extension)
• Affordability intent — "affordable IVF," "IVF cost in Greater Noida"
• Condition-specific — PCOS, low AMH, recurrent IVF failure, male infertility
• Review and reputation — "IVF clinic with good reviews," "most trusted IVF doctor"
• Comparison intent — "compare IVF centres," "standalone clinic vs hospital IVF"
• Brand / entity lookup — searching a clinic or doctor by name

How we tested

• Engines: Google AI Mode, ChatGPT (GPT-4o), Gemini 1.5 Pro, Perplexity
• Location: Noida, Uttar Pradesh, India (consistent across all tests)
• Period: May–June 2026
• Documentation: Screenshots captured for every result; entity names, source citations, and answer structure logged per query
• Repeat testing: Each query was run at least twice across different sessions to identify consistent patterns vs one-off results

How we scored visibility

• High: Clinic or doctor appeared prominently and repeatedly across test runs
• Medium: Appeared in some intents or some engines, not consistently across all
• Low: Weak signal or absent from the tested query set
• Not scored: Insufficient evidence from the available screenshot set

Important: AI search results are not static. They vary by date, session history, and ongoing model updates. These findings reflect consistent patterns observed across repeated testing — not fixed rankings.

2. Representative Results from the 25-Query Audit

Below is a representative selection from the 25-query audit — 9 queries shown, one per intent cluster. The downloadable dataset contains all 10 fully transcribed queries from the Google AI Mode testing phase, with Gemini and Perplexity results from the same sessions being transcribed progressively from the screenshot archive: Download the research dataset (10 queries, Google AI Mode fully transcribed — v1, August 2026) →

How to read this table: "Entities Observed" reflects what appeared in AI-generated answers during our testing — not a guaranteed or permanent result. Visibility scores reflect consistency across repeated test runs. A note on language: throughout this post, direct observations describe what we saw in results; interpretations describe what those observations may suggest; hypotheses flag possibilities for future testing. We flag which is which.

Data completeness note: The original study framework covers 25 queries across 4 engines. This dataset currently publishes 10 fully verified Google AI Mode observations. Gemini and Perplexity results from the same testing sessions are being transcribed from the screenshot archive and will be added to the downloadable dataset as verified. Only directly observed, screenshot-documented results are published — no data has been inferred or invented.

Table shows a representative selection from the full 25-query audit. "Aadya" refers to a boutique fertility clinic in Greater Noida West included in the audit with permission. Other clinic names are from publicly observable AI engine results. Full query log available on request.

3. Nine Findings from the Audit

Across 25 queries and four AI engines, nine patterns emerged with enough consistency to report. Each finding below identifies what we observed directly and, separately, what we interpret from those observations. Where we are offering a hypothesis for further testing, we say so.

Finding 1 — AI answers are entity-first, not website-first

Across every query, the AI engines assembled answers by combining clinic name, doctor name, location, treatment type, and reviews — not by evaluating website quality. A clinic with a poor website but a rich, consistent entity footprint across Practo, Google Maps, and third-party publications appeared more reliably than clinics with polished websites but limited external presence.

Interpretation: Our observations suggest that a provider's website is only one part of its AI visibility. AI-generated recommendations appeared to draw on a wider entity footprint — including official websites, Maps listings, directories, reviews, and third-party sources. Building a complete doctor entity across all platforms appears, from our testing, to be foundational to consistent AI visibility.

Finding 2 — Hyperlocal geography changes recommendations significantly

The query "IVF clinic near Gaur City 2 / Bisrakh" produced a different shortlist than "best IVF centre in Greater Noida West" — despite these locations being within a few kilometres of each other. Proximity signals dominated the hyperlocal query. Clinics that explicitly mentioned Gaur City 2 or Bisrakh in their content or GBP service areas appeared; clinics that only mentioned "Greater Noida" did not.

Observation: In our results, different localities produced different recommendation sets despite geographic proximity. Interpretation: This suggests that hyperlocal relevance and proximity signals may influence which providers appear in locality-anchored queries. Hypothesis for further testing: Clinics with content and GBP service area signals that explicitly name specific localities (Gaur City, Nirala Estate) may appear more consistently in those hyperlocal queries than clinics that reference only "Greater Noida West."

Finding 3 — Condition-specific queries favour individual doctors, not clinics

When the query was "IVF for PCOS" or "IVF doctor for low AMH," AI engines shifted from recommending clinics to recommending individual named doctors — specifically those with published content demonstrating expertise in those conditions. The clinic name was secondary; the doctor's condition authority was primary.

Observation: In our testing, condition-specific queries returned named individual doctors rather than clinic names — and these were not the same doctors named in general IVF queries. Interpretation: This suggests that condition intent and general local intent may be distinct visibility categories, requiring different content strategies. Doctor-level personal branding with condition-specific content appears, from our results, to be relevant to condition-intent queries in ways that clinic-level content alone may not address.

Finding 4 — Review queries are a completely separate battlefield

The query "IVF clinic in Greater Noida with good patient reviews" produced a different shortlist from every other query tested. Clinics that appeared strongly in general and comparison queries did not necessarily appear in the review query. The review query surfaced entities with high review density on specific platforms and, critically, reviews that contained substantive clinical detail — not just star ratings.

Observation: Review-intent queries surfaced a different set of providers than broad local discovery queries — including providers who were not prominent in earlier query clusters. Interpretation: In our tested results, providers associated with substantial review footprints across multiple platforms appeared more often in review-oriented queries than providers with reviews concentrated on a single platform. Hypothesis: Review quality and platform diversity may be more relevant to review-intent queries than review count alone — but our research does not reveal the weighting mechanism inside any AI engine.

Finding 5 — Comparison content is disproportionately valuable

The comparison query ("compare IVF centres in Greater Noida West") surfaced a boutique two-doctor clinic alongside hospital IVF departments with significantly larger infrastructure — purely because the AI engine had comparison-structured content to pull from. The smaller clinic had published a "how to choose an IVF clinic" guide that the AI engine cited as a source for its comparison answer.

Observation: A boutique two-doctor clinic appeared alongside hospital IVF departments in the comparison query. A "how to choose an IVF clinic" guide published by the smaller clinic was cited as a source in the AI answer. Interpretation: Comparison-oriented queries may create opportunities for smaller clinics to appear alongside larger healthcare brands, particularly when comparison-structured content exists on their site. Hypothesis: Publishing structured comparison or buyer's guide content may help AI engines construct comparison answers that include your clinic — but we cannot confirm this is causal from our query results alone.

Finding 6 — Cost intent is a distinct cluster with different winners

Affordability-intent queries ("affordable IVF in Noida Extension," "IVF cost in Greater Noida 2026") produced results dominated by chains and hospitals — Cloudnine, Motherhood, Yatharth — that have published transparent package information online. Boutique clinics without published cost guidance were largely absent.

Observation: The clinics that appeared in affordability-intent queries were all providers with some form of cost guidance published online — package pages, consultation fee information, or cost comparison guides. Boutique clinics without published cost information were not observed in this query cluster. Interpretation: Publishing cost transparency may be a prerequisite for affordability-intent visibility, not just a differentiator. This is not about pricing low — the largest chains appeared in this cluster, not the cheapest providers.

Finding 7 — Brand / entity queries can generate rich, comprehensive answers

When the query was the clinic's own name ("tell me about [clinic name]"), the AI engine assembled a comprehensive structured answer covering doctors, services, location, consultation fees, opening hours, and review summary — without the clinic having published any of this in a dedicated "about us" format. The engine synthesised it from across the clinic's GBP, Practo listing, website, and reviews.

What this means: Every factual field across every platform is a building block for your brand query answer. Inconsistencies — wrong phone number on Justdial, outdated hours on Practo, different doctor names across platforms — create gaps and errors in AI-generated answers about your clinic. A reputation and entity audit is not optional — it is foundational.

Finding 8 — AI engines distinguish hospital IVF departments from standalone clinics

Across multiple queries, AI engines explicitly separated "hospital IVF departments" (Yatharth, Cloudnine) from "boutique/standalone fertility clinics" (Aadya, Zeva, The Bliss) — and described both categories to patients without positioning one as superior. This means a two-doctor boutique clinic is not competing against a hospital on the hospital's terms. It is occupying a different AI answer category.

What this means: Lean into your positioning. A standalone clinic should explicitly state its care model — smaller team, more continuity, no departmental handoffs, same doctor throughout. That description occupies a specific AI answer slot that a hospital cannot fill.

Finding 9 — AI engines sometimes make strong qualitative claims from unverified sources

In several results, AI engines made claims about clinics — experience years, success rates, specific patient outcomes — that could not be verified as coming from the clinic's own published material. This appears to be a synthesis error where AI engines combine statements from multiple sources and attribute them imprecisely.

What this means: This is a risk, not just an opportunity. If an AI engine is attributing a success rate to your clinic that you never published — higher or lower — that creates a patient expectation problem. The solution is to publish verified, factual, carefully worded statements about your clinic's experience and approach on your own website and profiles, making it clear what you are and are not claiming. AI hallucinations about clinics are a documented problem — verified entity data is the best defence.

4. Observed Characteristics of Frequently Surfaced Providers

Across the 25 queries, five providers appeared with the highest frequency. Below we describe characteristics these providers share — drawn from publicly observable information on their websites and profiles. We are not asserting that these characteristics caused their AI visibility; we are describing what the frequently-surfaced providers had in common.

The pattern across all five: they share several observable characteristics — named doctors on condition-specific pages, cross-platform consistency, published cost guidance, active GBP profiles, and at least some third-party editorial mentions. Whether these characteristics are what caused their AI visibility, or are simply correlated with it, our query-level research cannot confirm. What we can say is that all five providers with the highest observed frequency shared this combination of signals.

5. The Action Plan — Specific, Prioritised, No Filler

The audit generated nine findings. Each finding maps to a specific action. Below is the prioritised plan in the order that creates the fastest compounding effect.

Critical — do this before anything else

Entity data audit. Before publishing a single new page, verify the exact spelling of every doctor's name, qualifications, registration number, experience, current clinic affiliation, address, hours, phone, and services — across your website, GBP, Practo, Lybrate, Justdial, and every other platform where the clinic appears. Any inconsistency reduces AI confidence in the entity. This is a spreadsheet task, not a content task. It costs nothing and takes one day. It is the foundation of everything else.

Doctor entity pages. Create or upgrade individual pages for each doctor at the clinic — not a generic "our team" page, but a dedicated URL per doctor, with their full credentials, conditions they treat, procedures they perform, publications or talks if any, and a short first-person statement about their approach. This is the page that wins condition-specific queries. Here is the full guide on building a doctor entity for Google and AI search.

High priority — do this in month one

Condition cluster content. For a fertility clinic, this means: one page per condition — IVF for PCOS, IVF for low AMH, IVF for recurrent failure, male factor infertility, donor egg IVF. Each page should be written under the treating doctor's byline, explain the condition in patient language, describe the clinic's approach, and include an FAQ section in static HTML. This is what wins Finding 3 — condition-specific queries currently dominated by doctors who have published condition authority.

Hyperlocal landing pages. One factual page per locality the clinic serves — Gaur City, Noida Extension, Bisrakh, Nirala Estate. Not thin location pages — pages that describe the clinic's distance and access from that locality, which doctors serve patients from that area, and what the consultation process involves. This closes the gap identified in Finding 2.

Review platform audit. Check the clinic's review presence on GBP, Practo, Lybrate, and Justdial. Respond to every existing review that has not been responded to. Identify the gap between your review count and the leading competitors. Build a process — not a gimmick — for encouraging genuine reviews from patients who have had a positive experience. This addresses Finding 4.

Medium priority — do this in month two

Comparison and how-to content. Publish at least one evidence-based comparison guide — "How to choose an IVF clinic in Greater Noida West: what to ask, what to compare, what to avoid." This directly feeds Finding 5 — comparison content surfaces smaller clinics alongside larger ones in AI comparison queries. The DoubleSure buyer's guide framework used in our healthcare marketing agency post applies directly here.

Cost transparency page. Publish a consultation fee and treatment cost guidance page with the clinic's explicit approval. Include a clear date, caveats about individualised treatment plans, and what is and is not included. This closes Finding 6 — affordability queries are currently invisible for clinics that publish no cost information.

Schema markup. Ask your developer to add MedicalBusiness schema, Physician schema for each doctor, FAQPage schema on every FAQ-containing page, and AggregateRating schema on the homepage. Validate at Google's Rich Results Tester. This is a one-time technical task with compounding benefits.

Ongoing — monthly

AI visibility tracking. Run the same core queries from this audit every month. Same queries, same four engines, same documentation format. Record whether each target entity appears, what position, what descriptor was used, what source was cited. Track in a spreadsheet. This is your benchmark — without it, you cannot measure whether any of the above is working.

6. What This Means for IVF Clinics in Greater Noida

The central finding of this study is not that one clinic consistently ranked above another. It is that AI healthcare recommendations changed substantially depending on the type of question asked.

In our tests, broad local discovery, hyperlocal proximity, condition-specific questions, reviews, affordability, and comparison queries often produced different recommendation sets. The same clinic that appeared prominently in a broad "best IVF clinic" query was absent from a condition-specific query — and vice versa. The same clinic that appeared in a comparison query did not appear in a review query.

For healthcare providers, this suggests that AI visibility should not be measured using a single keyword or a single "ranking." It should be evaluated across multiple patient-intent scenarios — because the patients who find you through a broad discovery query and the patients who find you through a condition-specific query are, in all likelihood, at different stages of their decision and looking for different things.

The nine findings in this post identify where those gaps exist and what actions may address them. None of them are guaranteed to produce AI visibility — our research identifies associations, not proven levers. But the work itself — building a complete entity, publishing condition-specific content, ensuring cross-platform consistency — is the same work that builds genuine clinical reputation. It does not require a large budget. It requires consistent effort over a six-to-twelve month horizon.

The clinics that begin this work in 2026, while AI patient discovery is still forming, are building positions that will compound. The ones that wait are ceding ground that becomes progressively harder to recover.

Related Research and Guides

• We Tested AI Search Across 4 Engines: How Patients Find Doctors in India — the general principles behind AI patient discovery
• How to Build a Doctor Entity for Google and AI Search — the step-by-step entity-building guide
• Is AI Telling Patients the Wrong Thing About Your Clinic? — what to do when AI engines get your details wrong
• Why Your Clinic Isn't Showing on Google Maps — or When Patients Ask AI
• GEO vs SEO for Healthcare Clinics: Which Should You Focus On in 2026?
• AI Visibility (GEO) — DoubleSure Service

Research source: DoubleSure internal AI Search Visibility Audit, May–June 2026. 25 queries tested across Google AI Mode, ChatGPT (GPT-4o), Gemini 1.5 Pro, and Perplexity. Location: Noida, Uttar Pradesh, India. Client entities referenced with permission. Competitor entity names sourced from publicly observable AI engine results. Full query log, screenshot documentation, and methodology notes available on request at info@doublesure.health.

Limitation: AI search results are not static. Findings reflect consistent patterns observed across repeated testing — not fixed rankings or guaranteed outcomes. Results may vary by date, session, location, and ongoing model updates. This research should be treated as directional evidence, not a definitive benchmark.

No commercial relationships with named entities: Competitor clinic and hospital names in this article are drawn from publicly observable AI search results. DoubleSure has no commercial relationship with, and does not endorse or recommend, any named competitor entity.