Benchmark Report

AI Visibility Benchmark 2026

An Independent Study of AI Visibility Across Indian D2C Brands

Author: Sanchari Sarkar - Publication ID: DP271-AVB-2026-001 - Version 1.1 - Published 19 July 2026

Independent ResearchAI VisibilityGEOAI Commerce

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Author: Sanchari Sarkar
Organization: Digital Platform 271
Publication Date: 19 July 2026
Audit period: July 2026
Sample: 30 brands
Publication ID: DP271-AVB-2026-001
Version: 1.1
Framework: AI Citation Readiness Framework™ v1.0
Status: Independent Industry Research (Not Peer Reviewed)

DOI: 10.5281/zenodo.22753158

For Journalists & Researchers

Research and media resources

Use the original report, one-page summaries, DOI and ready-to-copy citation when referencing this benchmark. These resources are public and intended to make verification and attribution straightforward.

31-page PDF

Full Benchmark Report

Methodology, findings, charts, limitations and dataset context.

1-page PDF

Executive Summary

Fast overview of the study and headline findings.

Press resource

Media Fact Sheet

Key statistics, methodology, citation details and press contact.

APA + BibTeX

Cite This Research

Copy the DOI-backed citation or download the citation file.

DOI: 10.5281/zenodo.22753158 Press: marketing@digitalplatform271.com

Downloadable visuals

Research graphics for media and publishing

Publication-ready PNG graphics summarizing the benchmark's headline findings. Journalists, researchers, writers and creators may reuse them with attribution to Digital Platform 271 and a link to this original research page.

AI Visibility Benchmark 2026 graphic showing that 24 of 30 Indian D2C brands were weakest in Recommendation Readiness.

24 of 30 brands were weakest in Recommendation Readiness

Hero research graphic highlighting the main benchmark finding from the AI Visibility Benchmark 2026.

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AI Visibility Benchmark 2026 comparison graphic showing AI visibility dimensions and Recommendation Readiness as the weakest dimension.

Being discoverable does not mean being recommendable

Comparison graphic showing the benchmark dimensions and the gap between discoverability and Recommendation Readiness.

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AI Visibility Benchmark 2026 quote card attributed to Sanchari Sarkar and Digital Platform 271.

Research quote card

Quote card summarizing the benchmark insight for journalists, writers and creators.

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Reuse credit: Digital Platform 271, AI Visibility Benchmark 2026. Please link to the original research page when publishing these visuals.

Research brief

Executive Summary

Digital Platform 271 audited 30 Indian D2C and FMCG brand websites in July 2026 to evaluate observable website evidence relevant to how AI systems can discover, understand, verify, cite, and consider brands for recommendation using the proprietary AI Citation Readiness Framework™. The final Version 1.1 edition clarifies the methodology, research-at-a-glance figures, charts, citations, and publication metadata for public reference.

Average Overall Score was 59.3, median score was 63, highest score was 73, and lowest score was 13. The study should be interpreted as independent industry research for the audited sample, not as a peer-reviewed market-wide ranking.

Average Overall Score: 59.3
Median: 63
Sample: 30 Brands
Framework: 53 Signals
Highest Score: 73
Lowest Score: 13

Key Findings

Signals that shape AI visibility

Recommendation Readiness was the weakest measured dimension for 24 of 30 brands, or 80.0% of the sample.

AI Discoverability was the strongest measured dimension for 26 of 30 brands, or 86.7% of the sample.

The average Overall Score was 59.3 and the median Overall Score was 63.

No audited brand achieved Grade A or B in this single-pass benchmark.

Benchmark at a Glance

Core descriptive statistics

59.3

Average Score

Median

11.86

Standard Deviation

13-73

Score Range

Brands Audited

Framework Signals

July 2026

Audit Period

India

Country

Benchmark Charts

Measured patterns from the sample

Average Scores by Dimension

Observed average score for each AI visibility dimension.

AI Discoverability16

Content & Entity Coverage12.9

AI Trust Signals11.3

AI Understanding11.2

AI Recommendation Readiness7.8

Category maxima are not published in this edition, so values are shown as observed scores without an invented denominator.

Source: Digital Platform 271 benchmark, July 2026

Grade Distribution

Number and percentage of brands in each grade band.

Grade A0 brands (0.0%)

Grade B0 brands (0.0%)

Grade C3 brands (10.0%)

Grade D16 brands (53.3%)

Grade E10 brands (33.3%)

Grade F1 brands (3.3%)

Counts and percentages are displayed explicitly; counts are not treated as percentages.

Source: 30 audited brands

Overall Score Distribution

Strip plot of all 30 verified Overall Scores.

Min 13Max 73

Mean: 59.3
Median: 63
Minimum: 13
Maximum: 73

Dot positions show individual audited brand scores. Mean, median, minimum and maximum are shown below the plot.

Source: 30 verified overall scores

Common Observed Patterns

Two aggregate proportions observed in the benchmark.

Recommendation Readiness was weakest

80.0%

24 of 30 brands

Discoverability was strongest

86.7%

26 of 30 brands

These proportions describe the audited sample only and should not be interpreted as market-wide prevalence.

Source: Digital Platform 271 benchmark, July 2026

Average Sample Profile

Dimension profile based only on observed sample averages.

Discoverability16

Content & Entity Coverage12.9

Trust Signals11.3

Understanding11.2

Recommendation Readiness7.8

This is not compared with an invented ideal brand and does not assume a published category maximum.

Source: Aggregate dimension averages

Conceptual AI Visibility Model

A practical way to organize AI visibility dimensions.

Discoverability

Understanding

Trust

Recommendation Readiness

Conceptual model only. This diagram illustrates a useful way to organize AI Visibility dimensions and should not be interpreted as a statistically validated causal sequence.

Source: Digital Platform 271 methodology

Methodology

How the benchmark is structured

Public website sample

The study reviewed 30 public Indian D2C and FMCG brand websites.

Single audit pass

Each website was audited once during July 2026 using the same proprietary framework.

Tracked evidence signals

The framework tracked 53 signals across five scoring dimensions.

Dimension scoring

Overall Score was calculated from dimension scores using the report methodology.

Interpretation boundaries

No repeated audits, formal confidence intervals, independent replication or signal-level public dataset are included in this edition.

Research Publication History

Version history

Version 1.0

Initial publication

Initial publication of the AI Visibility Benchmark 2026 research page and public assets.

Version 1.1

Editorial improvements

Research at a Glance
Methodology clarification
Charts updated
Citation improvements

Conceptual Model

How the report organizes AI Visibility

Framework Sequence

A conceptual structure for interpreting the benchmark.

1Discoverability
2Understanding
3Trust
4Recommendation Readiness

Conceptual model only. This diagram illustrates a useful way to organize AI Visibility dimensions and should not be interpreted as a statistically validated causal sequence.

The study separates whether a brand can be discovered from whether it is positioned to be recommended. This distinction matters because technical crawlability alone does not establish entity confidence, attribution, evidence or answer readiness.

The model is used as an editorial framework for interpreting findings. It is not presented as a causal model or a statistically validated sequence.

Recommendations

Strategic interpretation for brands

Treat crawlability and recommendation readiness as separate objectives.

Audit missing signals at brand level before applying generic fixes.

Strengthen entity clarity, evidence and third-party trust signals.

Publish useful, attributable and answer-ready information.

Re-audit over time to measure change.

Limitations

What this research does not claim

Small, non-random sample
Single audit pass
Proprietary methodology
No formal confidence intervals
No published per-signal breakdown
Findings are descriptive, not causal
Not statistically representative
Industry subgroups are too small for robust category comparisons

Funding and Conflict Disclosure

Transparency note

Funding: This research was self-funded by Digital Platform 271.

Conflict disclosure: Lunepop, one of the 30 brands included in the benchmark, is affiliated with the report author. Its audit result was retained in the dataset and included in aggregate calculations. Lunepop received an Overall Score of 42 and Grade E.

Citation

Cite This Research

APA citation

Sarkar, S. (2026). AI Visibility Benchmark 2026 (Version 1.1). Digital Platform 271 Research. https://doi.org/10.5281/zenodo.22753158

BibTeX citation

@report{Sarkar2026AIVisibilityBenchmark,
  author = {Sanchari Sarkar},
  title = {AI Visibility Benchmark 2026},
  institution = {Digital Platform 271 Research},
  year = {2026},
  month = {July},
  version = {1.1},
  number = {DP271-AVB-2026-001},
  doi = {10.5281/zenodo.22753158},
  url = {https://doi.org/10.5281/zenodo.22753158}
}

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Author

Sanchari Sarkar

AI Visibility Consultant, Digital Platform 271

Sanchari Sarkar develops practical frameworks and research for evaluating how brands are discovered, understood and recommended by AI-powered search and answer systems.

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Publisher

About Digital Platform 271 Research

Digital Platform 271 Research is the independent research and publications division of Digital Platform 271. We publish original studies, benchmarks, and practical frameworks on AI Visibility, Generative Engine Optimization (GEO), AI Commerce, and AI-driven digital discoverability. Our research helps businesses understand how AI systems discover, interpret, and recommend brands in the emerging era of AI-powered search.

Press contact: marketing@digitalplatform271.com.

References

Reference areas

Schema.org structured data guidance
Search engine crawlability and indexing documentation
AI search and answer-engine visibility research

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