AI Visibility vs. SEO Performance Case Study
A Comparative Analysis of Brand Authority, Citation Depth, and Generative Search Presence
Focus: BFSI | Study Date: May 2026 | Brands: Groww · AngelOne · ETMoney · ETMoney · Moneycontrol
This case study examines five Indian BFSI platforms – Groww, AngelOne, ETMoney, Screener.in, and Moneycontrol, to determine whether organic SEO performance predicts AI visibility in large language models. Using SEMrush data as of May 2026, we cross-reference organic traffic, branded and non-branded traffic in absolute numbers, traffic per page, total pages, backlinks, AI visibility scores, LLM cited page distribution, AI topic composition, and page type architecture.
The findings confirm that SEO is a necessary condition but insufficient on its own. Traffic efficiency (traffic per page), content type architecture, and brand strength are independent determinants of AI visibility, a gap most starkly illustrated by Moneycontrol, which holds the largest SEO footprint yet does not lead on AI visibility.
The core thesis
SEO gets you indexed. Content depth gets you discovered. Brand strength + content intent + traffic efficiency gets you recommended. These are three distinct outcomes. Only the third directly determines AI visibility. The data from this study makes that gap hard to dismiss.
1. The Question
Moneycontrol has 340,898 indexed pages, 53.6M organic traffic, 26.6 million backlinks, and 26,500 AI-performing topics. Groww has 25,424 indexed pages and 43.8M organic traffic. By every conventional SEO yardstick, Moneycontrol is the dominant player. IIn terms of keywords, Groww ranks for 345K+ keywords and Moneycontrol ranks for 892K+ keywords, with an overlap of 203K+ keywords. This means Groww shares 58% of keyword coverage while Moneycotrol shares merely 23%..
And yet Groww leads AI visibility by 11 points (70 vs. 59). This study investigates why and whether any SEO metric reliably predicts AI visibility across a set of five closely competing Indian BFSI platforms.
2. The Data at a Glance
All five brands were analysed across the following parameter sets, with India (IN) as the geographic filter throughout. Tables in all sections are sorted highest to lowest by AI visibility score.
2.1 Master Data Summary
Branded and non-branded traffic are absolute figures derived from SEMrush domain-level traffic split data.
| Brand | AI Score (/100) | Organic Traffic | Branded Traffic | Non-Branded Traffic | Total Pages | Backlinks | Total AI Topics | Traffic / Page |
| Groww | 70 | 43.8M | 2.6M | 41.2M | 25,424 | 192,700 | 8,300 | 1,723 |
| AngelOne | 65 | 7.5M | 300K | 7.2M | 50,000+ | 303,800 | 9,100 | 150 |
| Moneycontrol | 59 | 53.6M | 9.6M | 44.0M | 340,898 | 26,600,000 | 26,500 | 157 |
| Screener | 58 | 38.3M | 1.5M | 36.8M | 35,643 | 91,900 | 5,600 | 1,075 |
| ETMoney | 52 | 903K | 99K | 804K | 11,319 | 410,200 | 3,300 | 80 |
2.2 Key Observations
- Groww (70): Leads AI visibility with 43.8M organic traffic, 2.6M branded traffic, and the highest traffic-per-page efficiency (1,723). It has the fewest pages (25,424) yet extracts the most traffic value per page, a signal LLMs respect.
- AngelOne (65): Disproportionate AI performance relative to its traffic base (7.5M). Its 300K branded traffic is the lowest absolute branded figure in the group, yet it achieves the second-highest AI score, suggesting that brand type and content structure matter more than branded volume.
- Moneycontrol (59): The most instructive data point in this set. Highest organic traffic (53.6M), highest branded traffic (9.6M), most backlinks (26.6M), most AI topics (26,500) and still ranks 3rd on AI visibility. Scale accumulation without content intent alignment does not convert to AI authority.
- Screener (58): Strong organic base (38.3M) and solid traffic efficiency (1,075/page), but AI score is held down by a near-total dependency on stock data pages (92.2% of traffic), which are navigational by nature and weakly cited by LLMs.
- ETMoney (52): Lowest across traffic (903K), traffic per page (80), and AI topics (3,300). Zero branded AI topics in its sampled data. The brand awareness deficit is the primary variable separating ETMoney from the rest.
3. Does SEO Correlate With AI Visibility?
The table below tests seven traffic and SEO factors against AI visibility score. SEMrush rank and authority score were excluded from this analysis as they are composite output metrics, not independent SEO factors. All traffic figures are absolute numbers.
| SEO / Traffic Factor | Range | Correlation | Observation | Causation? |
| Organic Traffic | 903K–53.6M | Moderate | Groww (43.8M) leads AI score; Moneycontrol (53.6M, highest traffic) scores only 59 — traffic scale does not determine ranking | Partial |
| Branded Traffic (Abs) | 99K–9.6M | Weak | Moneycontrol has the highest branded traffic (9.6M) but does not lead AI visibility. ETMoney lowest on both, pointing to brand deficit rather than causation | No |
| Non-Branded Traffic | 804K–44M | Moderate | Groww’s 41.2M non-branded traffic aligns with highest AI score. Moneycontrol’s 44M non-branded is higher yet scores lower — content intent matters more than volume | Partial |
| Traffic per Page | 80–1,723 | Strong | Groww (1,723/page) leads both traffic efficiency and AI score. Moneycontrol (157/page) and AngelOne (150/page) have bloated page counts dragging efficiency. ETMoney (80/page) is lowest across both dimensions | Yes — partial |
| Total Pages | 11K–341K | None | Moneycontrol has 340,898 pages — 13x Groww — yet scores 11 points lower. More pages does not produce higher AI visibility | No |
| Backlinks | 91.9K–26.6M | None | Moneycontrol has 26.6M backlinks, 138x Groww’s 192.7K. No positive AI score relationship. Raw link volume is not an AI citation signal | No |
| Total AI Topics | 3.3K–26.5K | Weak | Moneycontrol has 26,500 AI topics (most in the group) but scores only 59. Screener has 5,600 and scores 58 — topic volume offers minimal advantage | No |
If you have found the case study useful so far, I have created a similar case study on AI visibility for the D2C beauty and skincare niche.
3.1 What This Matrix Shows
No single SEO factor directly causes higher AI visibility. Correlation where it exists (organic traffic, non-branded traffic) is moderate and filled with exceptions. The one factor that shows the strongest signal is traffic per page:
- Traffic per page is the closest proxy we found for AI visibility: Groww at 1,723 per page leads AI visibility (70). Screener at 1,075 scores 58. Moneycontrol at 157 scores 59. ETMoney at 80 scores 52. The relationship holds across all five brands, suggesting that page efficiency, meaning content that earns traffic because it genuinely answers queries, is what LLMs recognise as authoritative.
- Backlinks have no relationship with AI visibility: Moneycontrol’s 26.6M backlinks versus Groww’s 192,700, a 138x difference, produces no AI visibility advantage. Backlink volume measures page popularity, not content credibility.
- Total pages have an inverse signal: The two brands with the most pages, Moneycontrol at 340,898 and AngelOne at 50,000+, have below-average traffic per page. Large page counts without proportionate traffic signal thin or navigational content, which LLMs do not cite.
- Branded traffic volume does not determine AI score: Moneycontrol’s 9.6M branded traffic is 3.7x Groww’s 2.6M. Moneycontrol scores 11 points lower. Branded recall is not the same as recommendation authority.
The Moneycontrol Paradox
Moneycontrol holds the top position on five of seven factors in the table above: organic traffic, branded traffic, non-branded traffic, total pages, and backlinks. It also has the most AI-performing topics (26,500) and the most total cited pages across all LLMs (58,305). Yet it scores 59 on AI visibility, 11 points below Groww. The gap is not a data anomaly. It reflects a structural content architecture issue that scale cannot compensate for.
4. Branded vs. Non-Branded Traffic
This section uses absolute traffic numbers throughout. Percentage splits vary too widely with total traffic size to be useful as comparators across brands of different scales.
| Brand | AI Score | Total Organic Traffic | Branded Traffic | Non-Branded Traffic | Traffic/Page | Key Note |
| Groww | 70 | 43.8M | 2.6M | 41.2M | 1,723 | Strong non-branded base; AI score highest |
| AngelOne | 65 | 7.5M | 300K | 7.2M | 150 | Low total traffic but strong brand. AI Overview drives citation |
| Moneycontrol | 59 | 53.6M | 9.6M | 44.0M | 157 | Highest branded absolute (9.6M) but does not convert to AI lead |
| Screener | 58 | 38.3M | 1.5M | 36.8M | 1,075 | Strong non-branded base; AI score held back by content type |
| ETMoney | 52 | 903K | 99K | 804K | 80 | Weakest on all dimensions; brand deficit is primary drag |
What the data shows:
Non-branded traffic volume correlates partially with AI score, but is disrupted by content type: Moneycontrol’s 44M non-branded traffic exceeds Groww’s 41.2M, yet Groww leads on AI. The differentiator is what that non-branded traffic is for — Groww earns non-branded traffic from informational and educational queries; Moneycontrol earns it predominantly from stock price lookups and market data.
Branded traffic is a brand signal, not an AI signal: Moneycontrol’s 9.6M branded traffic reflects strong recall. But LLMs form recommendations from editorial mentions, third-party citations, and independent references — not from direct brand searches. Brand recall and brand authority are different assets.
AngelOne is the clearest outlier: 300K branded traffic (the lowest absolute figure) paired with 65 AI visibility. This breaks any assumption that branded traffic is a prerequisite for AI presence. AngelOne’s AI visibility likely derives from content specificity — its commodity and stock trading content is cited in LLM responses for a narrow but high-confidence set of topics.
5. AI Topics: What LLMs Actually Cite Brands For
Note: AI topic data is based on a sample export of 1,000 topics per brand from SEMrush (the maximum available per export). Totals used for headline counts reflect the actual full figures per SEMrush: Groww 8,300, AngelOne 9,100, Moneycontrol 26,500, Screener 5,600, ETMoney 3,300. The branded vs. non-branded split below is from the 1,000-topic sample.
| Brand | Total AI Topics | Branded Topics (sample) | Non-Branded Topics (sample) | Total Mentions (sample) | Branded Mentions (sample) | Avg Visibility Score | Avg Difficulty |
| Groww | 8,300 | 9 | 991 | 6,395 | 256 | 34.3 | 48.1 |
| AngelOne | 9,100 | 18 | 982 | 4,933 | 287 | 26.5 | 48.5 |
| Moneycontrol | 26,500 | 13 | 987 | 6,798 | 210 | 33.7 | 44.5 |
| Screener | 5,600 | 11 | 989 | 7,844 | 95 | 34.4 | 44.1 |
| ETMoney | 3,300 | 0 | 1,000 | 2,158 | 0 | 17.0 | 49.8 |
Key finding: Since the export size is capped at 1,000 topics for all brands, the non-branded share appears uniformly high (98 to 100 percent) across every brand. This is a sample artefact, with 1,000 topics drawn from a pool of 3,300 to 26,500, branded topics, which are typically fewer in total, have lower representation. The more meaningful comparison is the average visibility score across sampled topics, which ranges from 17.0 (ETMoney) to 34.4 (Screener), independently of sample size.
5.1 Top Topics Per Brand — What LLMs Associate Each Brand With
The following are the top topics by LLM mention volume from each brand’s sampled AI topic data. These are the specific queries where LLMs are citing these brands most frequently.
Groww — Top Topics by LLM Mentions
| Topic | Mentions | Visibility | Type | Branded? |
| Groww Investment App | 98 | 76 | Branded / App | Yes |
| DMart & Avenue Supermarts Shares | 66 | 39 | Stock Data | No |
| Brokerage Calculator & Fees | 57 | 40 | Tool/Calculator | No |
| India Tax Calculators | 55 | 28 | Tool/Calculator | No |
| Mutual Fund SIPs | 40 | 41 | Mutual Fund | No |
| Groww Customer Support & App Status | 33 | 88 | Branded / Support | Yes |
| Demat Account Basics | 35 | 31 | Educational | No |
Observation: AngelOne’s top AI topic is Gold Prices Today with 149 mentions, the second-highest single-topic mention count in the entire dataset. This maps directly to AngelOne’s commodity traffic dominance at 53.5% of organic traffic. The AI citation pattern mirrors the organic traffic pattern, confirming that commodity content is AngelOne’s strongest signal across both SEO and LLMs.
AngelOne — Top Topics by LLM Mentions
| Topic | Mentions | Visibility | Type | Branded? |
| Gold Prices Today | 149 | 20 | Commodity Data | No |
| Stock Market Education & Courses | 96 | 23 | Educational | No |
| SGX Nifty & Bank Nifty Live & Futures | 65 | 20 | Market Index | No |
| Angel One & Broking – Indian Stock Trading | 57 | 63 | Branded | Yes |
| India Silver Price Today | 51 | 23 | Commodity Data | No |
| Brokerage Calculator & Fees | 50 | 38 | Tool/Calculator | No |
Observation: AngelOne’s top AI topic is Gold Prices Today with 149 mentions — the second-highest single-topic mention count in the entire dataset. This maps directly to AngelOne’s commodity traffic dominance (53.5% of organic traffic). The AI citation pattern mirrors the organic traffic pattern, confirming that commodity content is AngelOne’s strongest signal across both SEO and LLMs.
ETMoney — Top Topics by LLM Mentions
| Topic | Mentions | Visibility | Type | Branded? |
| Mutual Funds SIP Calculator | 84 | 17 | Tool/Calculator | No |
| Indian Mutual Funds | 67 | 12 | Mutual Fund | No |
| India Tax Calculators | 29 | 21 | Tool/Calculator | No |
| Indian Fixed Deposit Rates | 27 | 11 | Rates Data | No |
| Indian SIP & Mutual Fund Tools | 18 | 42 | Tool/Calculator | No |
| Personal Finance Apps | 15 | 17 | App Category | No |
Observation: Every top topic for ETMoney is non-branded, and visibility scores are consistently low (11–42). The topics themselves are highly competitive (MF SIP calculators, Indian mutual funds), ETMoney appears in these LLM conversations but is not the dominant recommended source. The absence of a single branded topic in the top results is the clearest data signal of insufficient brand salience in LLM training contexts.
Screener — Top Topics by LLM Mentions
| Topic | Mentions | Visibility | Type | Branded? |
| Reliance Stock Price & Power (NSE/BSE) | 121 | 29 | Stock Data | No |
| Suzlon Energy Stock Prices | 62 | 38 | Stock Data | No |
| Bajaj Finance & Housing Finance Stocks | 51 | 29 | Stock Data | No |
| BEL and BHEL Stock Prices | 49 | 35 | Stock Data | No |
| Jaypee Group Shares Overview | 49 | 31 | Stock Data | No |
| Stock Screeners | 31 | 36 | Tool — Branded Adjacent | Yes (Adjacent) |
Observation: Screener’s entire top topic set is stock-specific data queries, exactly mirroring its organic traffic architecture (92.2% stock and equity pages). ChatGPT accounts for 73.9% of Screener’s cited pages, which aligns with the analytical, lookup nature of Screener’s content. However, the uniform “Stock Data” type across all top topics means Screener is cited for retrieval, not for editorial recommendations. This creates a structural ceiling on AI visibility score.
Moneycontrol — Top Topics by LLM Mentions
| Topic | Mentions | Visibility | Type | Branded? |
| Global Stock Indices | 61 | 30 | Market Data | No |
| Jaypee Group Shares Overview | 47 | 31 | Stock Data | No |
| FII/DII Market Data | 39 | 29 | Market Data | No |
| BEL and BHEL Stock Prices | 38 | 30 | Stock Data | No |
| MoneyControl Pro & APK Deals | 22 | 66 | Branded | Yes |
| Moneycontrol Portfolio & Apps | 16 | 81 | Branded | Yes |
Observation: Moneycontrol’s top non-branded AI topics are all market data and stock data queries, the same navigational content that dominates its organic traffic architecture (48.1% stock and equity). The highest visibility scores (66, 81) appear only on branded topics. This confirms that Moneycontrol is strongly recognised as a brand entity by LLMs, but its non-branded content, which forms the bulk of its 26,500 topics, achieves only moderate visibility scores (29 to 34 range). Brand recognition is present. Editorial citation authority is not.
6. LLM Cited Pages: Platform Distribution and What It Reveals
The table below shows the absolute number of pages cited per LLM platform for each brand, along with total cited pages and traffic per page. All tables sorted by AI visibility score.
| Brand | AI Score | Total Cited Pages | ChatGPT | AI Overview | AI Mode | Gemini | Traffic/Page |
| Groww | 70 | 17,400 | 5,300 | 6,700 | 3,300 | 2,100 | 1,723 |
| AngelOne | 65 | 21,600 | 4,000 | 10,300 | 4,700 | 2,600 | 150 |
| Moneycontrol | 59 | 58,305 | 30,600 | 18,500 | 9,200 | 5 | 157 |
| Screener | 58 | 17,000 | 7,400 | 5,200 | 2,600 | 1,800 | 1,075 |
| ETMoney | 52 | 7,432 | 2,900 | 1,900 | 1,800 | 832 | 80 |
6.1 What the Cited Pages Data Reveals
- Moneycontrol has the most cited pages in absolute terms (58,305), yet does not lead on AI score: This confirms the finding from the organic data. Volume of pages cited does not determine quality of AI visibility. Moneycontrol is cited across 58,305 pages, more than 3x Groww’s 17,400, yet scores 11 points lower. Cited page count measures indexation breadth in LLM systems, not recommendation authority.
- AngelOne punches above its weight (21,600 total cited pages, AI score 65): Its 10,300 AI Overview citations are the highest of any brand in this dataset on that platform. This maps directly to AI Overview’s function, which is surfacing high-relevance pages in Google search responses. AngelOne’s commodity and stock trading content appears to be structured in a way that Google’s AI systems judge as highly relevant to specific query types.
- The Moneycontrol Gemini anomaly is the most significant single data point in this section:Only 5 cited pages on Gemini. For a brand with 340,898 indexed pages, 53.6M organic traffic, and 58,305 total cited pages across other LLMs, this near-zero Gemini presence is a structural gap. Gemini is a Google product. Moneycontrol is Google’s top-ranked Indian BFSI domain by SEMrush rank (52). The fact that Gemini does not cite Moneycontrol at scale, while citing AngelOne (2,600 pages) and Groww (2,100 pages), suggests a content quality or format signal, not a discoverability issue. Likely causes include over-reliance on dynamic or real-time data pages that Gemini’s citation model does not index as editorial sources, and a navigational content architecture that fails Gemini’s quality thresholds despite passing Google’s ranking algorithm.
- Screener’s ChatGPT dominance (7,400 cited pages) with moderate AI score (58) illustrates the retrieval vs. recommendation gap: ChatGPT cites Screener for specific stock lookups — a retrieval function. But retrieval citations do not generate the same recommendation authority as editorial citations. Screener appears in LLM outputs when users ask for specific data; it is not consistently recommended when users ask for overall financial guidance.
| The Gemini Signal Moneycontrol: 5 Gemini cited pages. AngelOne: 2,600. Groww: 2,100. This is not a traffic or authority problem — Moneycontrol outranks both on every SEO metric. Gemini’s citation model appears to prioritise content that functions as a trustworthy editorial source over content that functions as a data delivery mechanism. Moneycontrol’s dominance in real-time market data, stock prices, and live indices — the content that earns its 53.6M organic traffic — is precisely the content type Gemini deprioritises for citations. This is a content strategy problem, not an SEO problem. |
7. Page Type Architecture: What Traffic Comes From, and Whether LLMs Care
One of the most underexamined variables in AI visibility analysis is page type composition. Not all organic traffic is equally citable. This section maps each brand’s page type distribution against their AI topic composition to identify mismatches.
| Brand | #1 Page Type (Traffic Share) | #2 Page Type | #3 Page Type | AI Topic Alignment | Highest Avg Traffic/Page Type | Key Implication |
| Groww | Stock/Equity (30.8%) | Market Index (22.5%) | Editorial/Learn (18.6%) | Yes — Brokerage, MF SIP, Demat (informational) | Market Index (114,785/pg) | Editorial depth supports broad AI citation |
| AngelOne | Commodity (53.5%) | Stock/Equity (19.8%) | Editorial/Learn (13.3%) | Partial — Gold prices, Stock courses align | Commodity (13,340/pg) | Heavy commodity skew; AI topics reflect this with Gold/Silver topics |
| Moneycontrol | Stock/Equity (48.1%) | Editorial/Learn (17.2%) | Other/News (13.6%) | Partial — Global indices, FII data align | Homepage (728,407/pg) | Stock data pages (48%) are navigational — low AI citability despite high traffic |
| Screener | Stock/Equity (92.2%) | Screener Tools (3.0%) | Homepage (3.0%) | Yes — Reliance, Suzlon, BEL/BHEL align directly | Homepage (381,899/pg) | Near-total stock data dependency; limited editorial depth limits AI score |
| ETMoney | Mutual Fund (31.9%) | Stock/Equity (30.4%) | Editorial/Learn (20.6%) | Yes — MF SIP calculator, Tax calculators align | Homepage (92,923/pg) | Editorial share (20.6%) is disproportionate to AI score — low visibility scores across topics |
7.1 Key Findings From Page Type Analysis
- Screener’s 92.2% stock data dependency is the clearest structural ceiling: Every top AI topic for Screener is a specific stock name query. These are lookup questions with factual, real-time answers — LLMs use Screener for retrieval but do not form recommendation associations with it for broader financial guidance. Screener has only 41 editorial/learn pages in its export, generating a combined 2,180 in traffic. There is effectively no editorial layer to build LLM recommendation authority from.
- AngelOne’s commodity dominance (53.5% of traffic) is unusual and works in its favour: Gold and silver price content generates high traffic per page (13,340 average) and appears in the top LLM topics (Gold Prices Today: 149 mentions, the highest single topic count in the dataset). The specificity of commodity data — daily, query-driven, high-intent — may make it more citable in certain LLM contexts than generic stock data pages.
- Moneycontrol’s editorial layer (17.2% of traffic from learn/news/editorial, 49,168 pages) is large in absolute terms but low efficiency (152 traffic per editorial page): This is the inverse of what drives AI citations. Groww’s 3,243 editorial pages generate 8.15M traffic at 2,515 per page. Moneycontrol’s 49,168 editorial pages generate 7.46M traffic at 152 per page. Moneycontrol has 15x the editorial volume but slightly less traffic from it, and at 16x lower efficiency. LLMs are more likely to cite a page that earns traffic because it genuinely answers a question than a page that exists but earns minimal engagement.
- ETMoney’s 20.6% editorial traffic share is disproportionate to its AI score: ETMoney dedicates a relatively high proportion of traffic to editorial content (20.6%), but its average visibility score across AI topics (17.0) is the lowest in the group. The issue is not page type — it is that the content, despite being informational, does not rank with sufficient authority within competitive topics like Mutual Fund SIP Calculators and Indian Mutual Funds, where Groww and Screener are stronger competitors.
8. Individual Brand Profiles
| Brand | AI Score | Brand Strength | Content Depth | SEO Scale | Paid | Key Insight |
| Groww | 70 | Strong | Strong | Moderate | High | All three layers operating. Non-branded dominance + editorial depth = AI citation leader |
| AngelOne | 65 | Moderate | Moderate | Moderate | High | AI Overview dominant (10,300 cited pages). Commodity content drives outsized traffic and AI presence |
| Moneycontrol | 59 | Strong | Moderate | High | Low | 58,305 cited pages (most in group) but Gemini = 5. Navigational content architecture limits editorial citability |
| Screener | 58 | Strong | Low | High | Low | ChatGPT dominant (7,400). 92% stock data pages = strong specificity but no editorial layer to support AI recommendations |
| ETMoney | 52 | Weak | Moderate | Weak | None | Zero branded AI topics. Lowest traffic/page (80). Brand awareness is the primary missing variable |
8.1 Groww — AI Score: 70
The benchmark. Groww’s lead in AI visibility is not explained by any single SEO factor. It emerges from the combination of the highest traffic efficiency (1,723 per page), a broad non-branded traffic base (41.2M) drawn from informational queries, and editorial content that earns genuine engagement. Its 8,300 AI topics span financial tools, calculators, equity data, and educational content — a mix that allows LLMs to cite Groww across a wide range of user query types, not just specific data lookups.
- Top AI topics mirror its strongest organic content: brokerage calculators, SIP mutual funds, demat basics — all informational, all high-traffic, all cited by multiple LLM platforms.
- Relies heavily on AI Overview (6,700 cited pages) — a concentration risk if Google’s AI citation model changes.
8.2 AngelOne — AI Score: 65
The efficiency case. AngelOne achieves 65 AI visibility on 7.5M organic traffic — the lowest total traffic base of any brand except ETMoney, yet the second-highest AI score. Two factors drive this:
- AI Overview dominance: 10,300 cited pages, the highest of any brand on that platform. AngelOne’s commodity and stock trading content appears structured to match Google AI Overview’s citation requirements.
- Gold Prices Today generates 149 LLM mentions — the highest single-topic figure in the dataset. AngelOne’s commodity content is highly specific, highly query-matched, and cited with confidence by LLMs.
8.3 Moneycontrol — AI Score: 59
The cautionary case. The most instructive brand in this study. Moneycontrol has the highest total cited pages (58,305), the most AI topics (26,500), and the strongest organic presence — yet scores 59. Three structural factors explain this:
- Content architecture mismatch: 48.1% of organic traffic comes from stock/equity data pages — navigational content that LLMs retrieve from but do not cite as editorial sources.
- Low editorial content efficiency: 49,168 editorial pages generating 152 traffic each — versus Groww’s 3,243 editorial pages at 2,515 each. Volume without quality is not a citation signal.
- Near-zero Gemini presence (5 cited pages): Inexplicable from a pure SEO standpoint. Likely a content quality threshold issue specific to Gemini’s citation model.
8.4 Screener — AI Score: 58
The structural ceiling case. Screener has a strong organic foundation (38.3M traffic, 1,075 per page) and ChatGPT dominance (7,400 cited pages). But 92.2% of organic traffic is from stock data pages — lookup queries for specific company prices. This is Screener’s greatest strength in SEO and its greatest liability in AI visibility. LLMs retrieve Screener for data; they do not recommend it for guidance. Without an editorial layer, this ceiling is hard to move.
8.5 ETMoney — AI Score: 52
The brand gap case. ETMoney’s 52 AI visibility score is not primarily a content problem or an SEO problem — it is a brand awareness problem. Zero branded AI topics in a 1,000-topic sample, average visibility score of 17.0 across those topics, and 80 traffic per page all point to the same diagnosis: ETMoney’s content exists but is not associated with recommendation authority in LLM training contexts. The primary lever is offline and online brand investment — PR, creator partnerships, editorial coverage — that builds brand mention density in financial contexts where LLMs learn to associate ETMoney with specific recommendations.
9. The Three-Layer Model
| Layer | Function | What it does | What it does NOT do |
| Layer 1: SEO | Gets you indexed | Ensures pages exist, are crawlable, rank for relevant queries, and accumulate backlinks | Guarantee AI citation — Moneycontrol proves this. 340K pages, 26.6M backlinks, AI score 59. |
| Layer 2: Content Depth | Gets you discovered | Editorial depth, informational intent coverage, high traffic-per-page efficiency, topic authority across LLMs | Override weak brand signals or a navigational-heavy content architecture that LLMs do not cite editorially |
| Layer 3: Brand + PR + Trust | Gets you recommended | Branded demand, third-party editorial coverage, cross-platform LLM citations, positive product reviews, independent validation | Be replaced by SEO or content investment alone. This layer must be built independently through product, PR, and marketing. |
These three layers are not sequential. Brands that invest in all three simultaneously accumulate AI visibility. Groww is the closest to running all three in this dataset. Moneycontrol has strong Layer 1 and visible Layer 3 (brand recall), but its Layer 2 suffers from a content architecture optimised for data retrieval rather than editorial authority.
10. Conclusion
AI visibility is a brand and content efficiency outcome, not an SEO outcome.
Groww (70) proves that AI visibility is achievable without the largest content footprint, if traffic efficiency, non-branded informational depth, and cross-platform LLM diversity are all in place.
Moneycontrol (59) proves that scale alone does not convert. The brand with the most pages, most traffic, most backlinks, most AI topics, and most total cited pages does not lead on AI visibility. The constraint is content architecture, with 48% navigational and stock-data pages that LLMs retrieve from but do not cite as editorial sources, and a near-zero Gemini presence that a brand of this size cannot afford.
ETMoney (52) proves that brand investment cannot be substituted. Zero branded AI topics, lowest average visibility score (17.0), lowest traffic per page (80). These three signals converge on the same diagnosis: LLMs have not formed a strong positive association with ETMoney as a recommendation source.
Screener (58) proves that structural page type dependency creates a ceiling. 92.2% stock data traffic is an SEO strength and an AI visibility constraint simultaneously, the same content that drives ranking does not drive LLM editorial citation.
Data: Semrush | All figures approximate
