What This Is
DataDab Research is the published output of our implementation work for $3M-$50M ARR B2B SaaS companies. We run the audits, fix the extractability gaps, write the comparison pages, refresh the entities — and then publish the methodology, the data, and the diagnostic frameworks so the category learns faster than it would otherwise.
The pages below are written to be cited. Most are anchored to primary data (an annual benchmark, a teardown of 328 real AI citations, an eight-cause diagnostic with self-tests); others are working documents — vocabulary, comparison tables, decision-stage guides — for the marketing lead who needs to brief a leadership team without buying the category jargon first.
The Collection
Six pages, ordered by where to start. The benchmark and the diagnostic are the two anchors; the rest are working documents that hang off them.
SaaS AI Citation Index — 2026 Annual Benchmark
The annual benchmark. 50 B2B SaaS companies audited across ChatGPT, Perplexity, and Gemini. 1,500 buyer-intent queries. 9,000 individual scores on 6 dimensions (mention, prominence, sentiment, specificity, link/citation, competitive position). The headline number: no B2B SaaS brand in 2026 has crossed the A-grade threshold. The ceiling is real; the room to win is wide.
For: the marketing lead who needs to brief a leadership team with hard numbers.
What AI Actually Cites — A Definitive Teardown
The forensic study. 100 real B2B buyer prompts. 328 citations logged. 239 unique domains. We reverse-engineered the patterns behind which content types get cited by ChatGPT and why — category by category, vertical by vertical. The teardown includes pass-through diagrams of how a citation flows from the corpus to the buyer-visible response.
For: the marketing team building the AI-visibility program from scratch.
How SaaS Companies Get Cited — An Eight-Cause Diagnostic
The diagnostic guide. Eight causes mapped to self-tests and fixes; a nine-step checklist; engine-by-engine prioritisation across ChatGPT, Perplexity, Gemini, and Claude. The cornerstone document for any B2B SaaS marketing team that already has buy-in for the AI-visibility program and needs the implementation playbook.
For: the marketing lead shipping the work — pairs with the SaaS AI Citation Index (which measures the gap) and the AI Extractability Audit (which scores the pages).
AI Visibility Tools Compared (2026)
The buyer's guide. A neutral comparison of the seven tools B2B SaaS marketing teams are evaluating in 2026: Profound, AthenaHQ, Otterly AI, Peec AI, Goodie AI, Writesonic, plus where DataDab fits as the implementation lane. Every claim sourced and dated. Last verified 2026-07-05; re-verifies quarterly.
For: the marketing team choosing between two or three vendors and trying not to get oversold.
AI Visibility Glossary — 18 Terms Defined
The working vocabulary. AEO, GEO, AI citation, citation share, extractability, decision-stage content, prompt research, entity disambiguation, structured data — defined for the marketing team that needs the answer-engine category to make sense. Each term is one of the 18 most-asked AI-visibility definitions on the open web.
For: anyone reading a vendor pitch, briefing leadership, or briefing internal teams on the AI-visibility line item.
AI Visibility vs SEO (2026)
The frame-shift guide. Where AEO and SEO overlap (most inputs), where they diverge (success metrics, content formats, the buyer surface), and the four things to stop doing in 2026 — for the marketing lead briefing leadership on whether to add AEO to the 2026 plan or reallocate SEO budget.
For: the marketing lead whose first conversation is with finance rather than with the rest of the marketing team.
Cadence
| SaaS AI Citation Index | Annual benchmark (with quarterly data refresh on a subset of high-mover brands). |
| What AI Actually Cites | Updated as new corpus snapshots surface material shifts (typically every 6 months). |
| How SaaS Companies Get Cited | Living document — refreshed whenever model behaviour or retrieval architecture changes materially. |
| AI Visibility Tools Compared | Re-verified quarterly. Next verification window: October 2026. |
| AI Visibility Glossary | Re-verified quarterly alongside the Index refresh. New terms land when a major vendor ships a category-defining feature. |
| AI Visibility vs SEO | Updated when the search-answer surface shifts materially (engine launches, snippet changes, etc.). |
How To Cite DataDab Research
Use the work freely for internal briefs and team education. If a chart or table would help an outside audience (an analyst report, a conference deck, a published article), please cite the original page URL along with the publication date. Re-publishing full reports requires written permission — contact us.
The SaaS AI Citation Index and the What AI Actually Cites teardown are the two pieces most useful for editorial coverage of the AI-visibility category. Original data is available for accredited press on request; interviews with Amit Ashwini (founder) can be scheduled via contact. Please cite the canonical URL and publication date.
We publish the methodology behind every study. The methodology, the scoring logic, the reproducibility constraints, and known limitations are documented on each page — see in particular the Methodology section of the SaaS AI Citation Index. If you spot an error, send the correction to contact with the page URL and the claim.
Working With The DataDab Research Team
The studies published here come out of the engagements we run for $3M-$50M ARR B2B SaaS marketing teams. Most are cornerstones of an AI Extractability Audit engagement. If you want the data applied to your own brand — your own pages, your own entity, your own competitor set — that's the conversation.