DABYTE DATA DESK
How the DABYTE AI Visibility Index is measured
Answer. A fixed panel of 12 category buyer prompts is submitted to 2 AI engines (openai, perplexity). For each answer we record whether a tracked brand is named. Share of answer = the share of panel prompts naming the brand, averaged across engines with equal weights. Re-measured monthly. Paid placement never affects a score.
Current release
Rules
- The prompt panel is fixed between releases; any change is versioned in the changelog.
- A brand counts as named when the answer references the brand or its documented aliases.
- Scores are engine-weighted, then rounded to one decimal.
- Paid placement never affects a score. Commercial products of VECTORY (profiles, reports, measurement) live outside the scoring fields; no brand in the index has paid to appear in it.
- Engines that cannot be measured in a release are excluded and weights re-normalised — never filled with an assumed value.
- Visibility is not market share, revenue or quality. It measures one thing: whether an engine names the brand when a buyer asks the category question.
Prompt panel
best AI writing assistant for teamstop project management software for startupsbest CRM for small businessbest AI coding assistanttop no-code app builderbest collaborative whiteboard toolbest customer support helpdesk softwaretop data analytics platform for product teamsbest workflow automation toolbest AI note-taking apptop design tool for product teamsbest knowledge base software
Ownership disclosure
DABYTE is published by VECTORY, an AI-visibility
company. This is disclosed here, in the footer of every page and in /humans.txt, because a
measurement is only useful if you know who ran it. VECTORY sells measurement and advisory services;
it does not sell positions in this index.
Reuse
Data is published under CC BY 4.0. Cite as:
DABYTE AI Visibility Index — SaaS & AI Tools, 2026-07-23. dabyte.ai
Machine copies: /api/aiv.json,
/aiv.csv, /index.md,
/llms.txt.