Key points
- An SEJ columnist counted 194 AI visibility tools launched in two to three years, using Product Hunt, G2 and LinkedIn as sources.
- Per the author, a little over 60% are bootstrap or solo projects, and startup mortality runs between 55% and 60%.
- Recommended fix: keep your own query set, store raw exports (response text, timestamps, source URLs, prompts) in your own infrastructure.
- Author argues no standard defines AI visibility, so each tool uses its own proprietary index and swapping vendors is not like for like.
Why it matters for marketers: Teams buying AI visibility software risk locking in unstable vendors and metrics; owning queries and raw data keeps reporting usable if a tool changes or disappears.
A new column in Search Engine Journal by Taylor Dan argues that marketers should not let a software platform define their AI search strategy. The author says leadership tends to reach for dashboards when facing unknowns, repeating the playbook from traditional rank tracking.
To test how mature the category is, the author searched Product Hunt, G2 and LinkedIn and found 194 tools launched in the past two years aimed at improving brands' AI visibility. By the author's analysis, a little over 60% look like side projects or solo founder builds, and failure rates run between 55% and 60%. The column points to cost as a driver: running multi-turn prompts with live browsing is far more expensive than scraping search results, which leaves thin margins for a $100 a month self-serve product.
The author also flags a measurement problem. With no accepted standard, each vendor builds its own proprietary index, so switching tools means switching definitions, not just exporting data. Citations in generative answers can also drift with no change to a brand's site or competitors, so a rank-tracker mindset can lead teams to chase phantom drops and gains.
The recommendation is to own the foundations. Keep an internal query matrix built around customer intent, comparison queries and category questions, documented independently of any subscription. Maintain your own prompt stacks to measure citation drift. Favor vendors that offer webhook exports, BigQuery connectors or CSVs so raw responses, timestamps, source URLs and prompts stay in your hands. The author adds that visibility is an outcome: in the author's experience, models cite brands with authoritative entity definitions, clear structure and active digital footprints, not brands that bought software.
Sources
This brief is our own summary. Read the full reporting at the source.