India’s AI Visibility Leaders 2026: Full Research Dataset

Introduction

This page hosts the complete research dataset behind Best SEO Agencies in India for AI Search (2026) — every candidate agency evaluated, the evidence behind each verification decision, and the raw technical findings from our AI-readiness audit.

We publish the underlying dataset because a ranking is only as trustworthy as the research that can be examined behind it.

The main report presents the ranking, analysis and editorial findings. This page provides the underlying research data, captured technical evidence and documentation supporting that work.

Download the Research Dataset

Research Dataset (.xlsx)

Download India’s AI Visibility Leaders 2026 — Research Dataset (.xlsx)

The complete research workbook containing the 96 candidate agencies, verification status, evidence-screening data, the 23-agency evidence and technical-audit summary, final Top 10 scoring with live formulas, a complete data dictionary, and a condensed methodology summary.

Raw Technical Evidence (.pdf)

Download India’s AI Visibility Leaders 2026 — Raw Technical Evidence (.pdf)

The captured robots.txt, llms.txt, and structured-data (JSON-LD) content for all 23 shortlisted agencies, reproduced as found during the July–August 2026 audit without editorial modification.

This is the primary source material behind our technical-readiness findings.

The evidence was manually captured from each agency’s publicly accessible technical endpoints and reproduced without editorial modification. It is intended to allow independent inspection of the technical conditions recorded during the research cycle.

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How This Data Was Collected

This edition evaluated 96 candidate agencies discovered across six independent sources using an identical query set:

• Google Search
• Google AI Overview
• Google AI Mode
• ChatGPT with web access
• ChatGPT without web access, tested separately as a methodology baseline
• Gemini
• Perplexity

Candidates were verified for eligibility before evidence screening began. Discovery and evaluation were deliberately kept separate throughout the research process.

Important: Before publication, reconcile the numerical funnel in this section against the underlying research workbook. The current research materials reference both 96 → 87 → 78 → 62 → 23 → 10 and the statement that 15 candidates were excluded outright. These categories must be made internally consistent and clearly defined rather than relying on implied distinctions.

Evidence screening rated each eligible agency’s publicly available proof as High, Medium, or Low.

A technical audit then examined the shortlisted agencies’ own websites for the AI-readiness infrastructure included in the research methodology, including:

• llms.txt
• structured data / JSON-LD
• AI-crawler accessibility

Manual Technical Verification

A significant methodological finding from this research was that automated research tools were not consistently reliable at detecting technical implementations.

Several agencies that manual verification showed to have substantial implementations were initially reported by automated tools as having none.

For that reason, technical findings in this dataset reflect manual verification rather than automated detection alone.

The accompanying Raw Technical Evidence PDF preserves the captured technical source material used in that verification.

For the complete ranking methodology, see How We Ranked These Agencies.

Time-Sensitive and Personalized Results

Results from AI and search platforms are time-sensitive and can be personalized.

Running the same discovery process again — particularly from a different location, device, account or personalization state — may produce natural variation from the results recorded in this dataset.

That is an inherent property of the systems being studied, rather than necessarily an error in this dataset.

This research therefore represents one consistent research baseline from the July–August 2026 research cycle, not every possible result that another researcher might obtain.

Data Dictionary

Every status term, score and column used in the dataset is defined in full inside the downloadable workbook’s Data Dictionary tab.

This includes definitions for:

• Verified
• Needs Verification
• Excluded
• High / Medium / Low evidence tiers
• technical-audit findings
• scoring fields
• other dataset-specific terminology

We keep the full definitions in the workbook rather than duplicating them here so there is a single authoritative place for the dataset definitions and less risk of the page and workbook drifting out of sync.

Known Limitations

This dataset reflects a single research cycle conducted during July–August 2026.

Agencies, their websites and AI platforms change quickly. The dataset should therefore be treated as a research snapshot rather than a live representation of current conditions.

Additional limitations include:

• Two agencies in the technical audit have incomplete data on specific sub-checks. These are explicitly identified in the workbook rather than estimated or filled with assumptions.
• AI and search discovery results are inherently time- and location-sensitive. This dataset represents one consistent, signed-out research baseline rather than every possible result another researcher might see.
• Evidence screening reflects publicly available information at the time of research. Private client data, NDAs and unpublished work cannot be reflected in the dataset.
• Technical conditions recorded in the Raw Technical Evidence PDF reflect what was publicly accessible and captured during the research period. Subsequent changes to an agency’s website or technical infrastructure are outside the scope of this edition.

Citation & Reuse

Suggested citation

The Digital Today Research (2026). “India’s AI Visibility Leaders 2026: Best SEO Agencies in India for AI Search — Research Dataset.” Retrieved from https://thedigitaltoday.in/research/indias-ai-visibility-leaders-2026-research-dataset/

This dataset is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You may cite, share, reproduce, adapt, and reuse the dataset, including for commercial purposes, provided appropriate attribution is given and any modifications are indicated.

Full reuse terms: Terms

Questions about a conflict or relationship worth knowing about: Disclosure Policy

About The Digital Today Research

This dataset is part of India’s AI Visibility Leaders, The Digital Today’s ongoing research series examining AI Search, AI visibility, GEO, AEO and related developments in the Indian digital ecosystem.

The Digital Today publishes the underlying research separately from its editorial rankings so that readers, researchers, publishers and other interested parties can inspect the evidence, methodology and data behind its conclusions rather than relying solely on the published ranking.

This dataset represents the 2026 research cycle and should be understood as a dated research snapshot. Agencies, websites, search platforms and AI systems change over time; subsequent editions may therefore produce different findings.

Editorial / Dataset Disclosure

This dataset is published as a research-supporting asset for the associated editorial report.

The Digital Today distinguishes between:

• the underlying research data and evidence;
• the methodology used to evaluate it;
• the editorial interpretation of those findings; and
• any commercial or other relationships disclosed under the site’s applicable policies.

See the Disclosure Policy for further information.

Last updated: 20.08.2026

This dataset is reviewed alongside the main report. See our Corrections Policy for how corrections and subsequent updates are handled.

See the findings and interpretation:
Best SEO Agencies in India for AI Search (2026): Top 10 Ranked.

More in this series:
India’s AI Visibility Leaders — All Editions