The Bollywood AI Visibility Index: How AI Engines Cite Indian Cinema

By the Everything-PR Research Team

EPR Editorial Team · Updated July 2026

The Bollywood AI Visibility Index is Everything-PR's running measurement of how the five major AI engines — ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews — cite Indian cinema. Thirty stars. Five engines. Eight prompts per cycle. Three runs per prompt. The recurring finding across every entry in the series: the engines over-cite the Mumbai-Hindi commercial tier — Shah Rukh Khan, Aamir Khan, Amitabh Bachchan — and systematically under-cite the prestige acting tier, the South Indian industries (Tamil, Telugu, Malayalam, Kannada), and the women who anchor India's most acclaimed work.

Key findings across the series

Why this series exists

Indian cinema is the world's largest film industry by output and one of the largest by revenue. The English-language press infrastructure that the AI engines weight in retrieval does not reflect that scale. The gap shows up the same way every cycle: ask any engine "who is the biggest Indian movie star" and the answer clusters around Shah Rukh Khan, Aamir Khan, and Amitabh Bachchan — three names from the Mumbai-Hindi commercial tier. Ask the same engines to name the highest-grossing Indian star of the last five years, and the answer is wrong. Ask them to name the prestige acting tier, and they reach for commercial leads instead.

This index measures the gap, names it, and updates it. The measurement layer — how any brand or category can track its own engine footprint the same way — sits in The Citation Share Index.

The series — all entries

1. Bollywood AI Visibility Index 2026: 30 Stars, 5 AI Engines, and the South India Blind Spot — The master ranking. Thirty Indian film figures across Hindi, Tamil, Telugu, Malayalam, and Kannada cinema, scored by Citation Share across the five engines. The South India blind spot, the female-leads gap, the most overrated and most underrated names. Published June 15, 2026.
2. The Allu Arjun AI Visibility Score — Citation Share of 54, ranked #17 of 30. By box office, one of the three most commercially successful Indian stars of the last five years. The gap between the receipts and the engines is the story. Published June 18, 2026.
3. Pushpa 2 vs Wicked: How AI Engines Treat Two Record-Breaking Films — Both broke records in December 2024. Three of five AI engines could not correctly identify the highest-grossing film of the month. The comparison reveals how the engines treat Western and Indian cinema differently. Published June 21, 2026.
4. The Crore Problem: How AI Engines Mistranslate Indian Box Office — AI engines get Indian box-office math wrong in roughly one-third of responses. Stale exchange rates. Crore-to-million confusion. Confident wrong answers. The engine-by-engine scorecard on a $400B film market. Published June 24, 2026.
5. The Prestige Tier AI Engines Cannot See: Bajpayee, Tripathi, Siddiqui, Sethupathi — Manoj Bajpayee, Pankaj Tripathi, Nawazuddin Siddiqui, Vijay Sethupathi, Tabu, Konkona Sen Sharma, Rajkummar Rao, Fahadh Faasil, Shefali Shah. Recognition rates of 30–55% on the engines while Mumbai-Hindi commercial leads clear 90%. Published June 27, 2026.

Methodology

Every entry in the series runs the same protocol: prompts engineered to elicit a specific category of answer (biggest star, prestige tier, highest-grossing, most acclaimed, regional leadership), three runs per engine, scored on Citation Share — a composite of Frequency (40%), Rank (25%), Industry Accuracy (20%), and Factual Accuracy (15%). The full methodology spec lives in The 5W AI Visibility Index: Methodology. Cross-vertical comparison work is centralized in The AI Visibility Index Franchise.

Why this matters commercially

Streaming-platform programming, brand endorsements, international festival circuits, and pre-greenlight talent research all increasingly run through AI-engine queries. The blind spot is not academic. It is operational. Brands and platforms making ten-figure decisions on the basis of engine-surfaced shortlists are working from a citation set that systematically under-represents the actors and industries that anchor the most acclaimed Indian work.

The parallel dynamic across other verticals — where the engines cite from a narrower press base than the market itself — is examined in Who Controls AI Answers.

What's coming next

• The Tollywood Economic Story — Telugu cinema's revenue scale and how AI engines describe it.
• The AR Rahman Test — how AI engines describe Indian film music as a discipline.
• The OTT Citation Index — which Indian streaming platforms the engines name first.
• The Malayalam Question — Mollywood's critical reputation versus its engine footprint.
• The Women of Indian Cinema Index — Alia Bhatt, Deepika Padukone, Vidya Balan, Tabu, Shefali Shah, Nayanthara, Nithya Menen — Citation Share by engine.

Related research

• The AI Visibility Index Franchise — Everything-PR's master index of all AI Visibility studies across verticals.
• The 5W AI Visibility Index: Methodology — full scoring framework.
• The Citation Share Index — the measurement layer for any brand or category tracking its engine footprint.
• Who Controls AI Answers — which publications the engines cite most.
• All Bollywood coverage on Everything-PR — including legacy reporting on Indian cinema PR, agency landscape, and crisis case studies.