AI Visibility Benchmark 2026

How to cite this report

Readers citing individual statistics should cite the report version and page and may request the underlying dataset from the author for verification.

Editorial policy, funding and conflicts of interest

This report presents findings from a single-pass proprietary audit conducted by Digital Platform 271 using its AI Citation Readiness Framework™. It is published as industry research for founders, marketers and consultants working on AI visibility and Generative Engine Optimization (GEO). It has not been peer-reviewed.

Funding: independently funded by Digital Platform 271. No external funding or sponsorship was received.

Executive summary

Digital Platform 271 audited 30 Indian D2C and FMCG brand websites in July 2026 using its proprietary AI Citation Readiness Framework™, which scores brands across five dimensions: AI Understanding, AI Trust Signals, AI Discoverability, AI Recommendation Readiness, and Content & Entity Coverage.

The sample is small and non-random and should not be interpreted as statistically representative of the Indian D2C market.

Key findings

• Recommendation Readiness was the weakest measured dimension across the sample, with an average score of 7.8.
• AI Discoverability was the strongest measured dimension, with an average score of 16.0.
• Recommendation Readiness was each brand's single lowest-scoring dimension in 24 of 30 cases (80.0%).
• AI Discoverability was each brand's single highest-scoring dimension in 26 of 30 cases (86.7%).
• The average Overall Score was 59.3, median 63, standard deviation 11.86, with scores ranging from 13 to 73.
• No audited brand received Grade A or B. Three received C, 16 D, 10 E, and one F.
• Content & Entity Coverage averaged 12.9, AI Trust Signals 11.3, and AI Understanding 11.2.

Scope of the study

In scope

• 30 Indian D2C and FMCG brand websites.
• Five scoring dimensions.
• A single point-in-time audit pass conducted in July 2026.

Out of scope

• Brands outside India and categories not represented in the sample.
• Paid media, social commerce, marketplace listings and offline retail presence.
• Causal attribution of scores to individual technical signals.

The final sample was drawn from an initial pool of 40 identified candidate websites. The source material does not break down the 10-brand difference between duplicate listings removed and inaccessible websites.

Research methodology

The AI Citation Readiness Framework™ v1.0 tracks 53 AI-related signals across five categories. Each brand's Overall Score is the sum of its five category scores.

The exact scoring rubric, point weighting and maximum possible score per category remain proprietary and were not included in the source dataset for this publication.

Results and interpretation

Overall score distribution

Category averages

Industry implications

If AI-powered search and assistants increasingly mediate how consumers discover and evaluate D2C brands, the gap observed in this sample reframes the question from “can AI find us?” to “would AI actively suggest us?” The concentration of grades in the C–E range suggests that recommendation-level readiness, as defined by this framework, was still underdeveloped across most audited brands at the time of the audit.

Common observed patterns

• Recommendation Readiness was the lowest-scoring dimension for 24 of 30 brands.
• AI Discoverability was the highest-scoring dimension for 26 of 30 brands.
• 27 of 30 brands scored between 14 and 17 on AI Discoverability.

The dataset contains category-level scores but not the per-signal breakdown needed to claim that a specific technical element drove an individual brand's score.

Recommendations

• Treat being crawlable and being recommended as distinct goals.
• Obtain a signal-level breakdown before investing in technical fixes.
• Treat AI visibility as an emerging, largely unsolved area of investment rather than a checklist.
• Do not use the aggregate figures as category-specific benchmarks for thinly represented industries.
• Future editions should resolve methodological gaps, publish more detail and continue to disclose affiliated brands.

Limitations

• Small, non-random sample of 30 brands.
• Not statistically representative of the wider Indian or global D2C market.
• Single audit pass in July 2026.
• Proprietary, undisclosed scoring weights and category maxima.
• No formal confidence intervals or significance tests.
• No signal-level evidence.
• Internal inconsistency in the source Evidence Confidence summary chart; per-brand master data is treated as authoritative.
• Affiliated brand included in the sample.
• Heterogeneous “Other” industry grouping.
• Descriptive and correlational, not causal.

Future research

• Expand sample size and document a defined sampling frame.
• Conduct repeated audits over time.
• Publish signal-level scoring data.
• Publish scoring weights and category maxima.
• Seek independent replication of a subset of audits.
• Continue explicit affiliated-brand disclosure and consider aggregate statistics with and without affiliated brands.

Conclusion

Across 30 Indian D2C and FMCG brands audited in July 2026, Recommendation Readiness was consistently the weakest of five measured AI visibility dimensions, both on average (7.8) and at the individual-brand level (the lowest-scoring dimension for 80% of the sample). AI Discoverability was consistently the strongest, both on average (16.0) and individually (the highest-scoring dimension for 86.7% of the sample). No brand reached the framework's top two grade bands.

These findings describe a specific, non-random sample audited once using a proprietary and only partially disclosed methodology. They are offered as directional industry research, not as a statistically representative account of the Indian or global D2C market.

Editorial clarification for the public web edition: the recommendations above identify methodological improvements that should be incorporated into future editions of this benchmark.

Appendix A — Full brand-level dataset (n=30)

Appendix B — Top and bottom performing brands

Top 10 by Overall Score

Blue Tea 73 · Foxtale 71 · Stonefield 70 · Lemme Be 69 · Flychem 69 · Clarion India 68 · Anveshan 66 · Conscious Chemist 66 · Gaia Fresh 65 · Zoff Foods 65.

Bottom 10 by Overall Score

Isak Fragrances 13 · Lunepop 42 · Demolish Foods 44 · Siroyaa 44 · Suri Fresh Extract 50 · Future Farms 52 · Matt Look 55 · Dot & Key 55 · AgroStarC 58 · Farmley 59.

Appendix C — Framework overview

• Framework: AI Citation Readiness Framework™
• Version: 1.0
• Signals tracked: 53
• Scoring categories: 5

Signals referenced but not quantified

Founder Identity Structured Data, Product Structured Data, FAQ Schema, Website Schema, Privacy Policy, and LocalBusiness Schema are named in the source material but were not accompanied by counts, percentages, brand impact or priority data in this audit pass.

Version history

Version 1.0 · 19 July 2026 — First public release.