Quick Ai visibility tracking of my site using Api #aitracking #airanking #aiseo
Summary
Anupam Pathak's short YouTube tutorial walks through the Serpent AI Rank API playground. The presenter picks the AI ranking option, enters a seed keyword such as "football api" along with a target domain, and runs one request. The results show whether the brand appears in answers from ChatGPT, Claude, Gemini and Perplexity.
The results show the URLs each engine cited, whether the brand was present and where each source appeared. The video also covers two prompt types, standard and deep, which are meant to separate different kinds of search intent. The product page and documentation describe the same endpoint and the pricing that goes with it.
Demonstration by Anupam Pathak, embedded from YouTube. AIOTruth did not reproduce the test.
Why it matters
Anyone trying to get recommended by AI needs a repeatable way to see which assistants mention their domain for the queries buyers actually type, and which competing sources get cited in their place. A single call that covers four engines turns that into something a team can run on a schedule, rather than typing prompts into each assistant by hand. The catch is that the output is only as good as the seed keywords and prompt types someone picks. AI answers also change from one run to the next, so one result shows a moment, not a stable position.
Source
- apiserpent.com/ai-rank-api the item itself, published by Anupam Pathak, created by Anupam Pathak
- apiserpent.com/blog/geo-vs-aeo-vs-seo-google-ai-search-guide Reproducible first-party demonstration, first party
- apiserpent.com/blog/ai-citation-tracker-python Reproducible first-party demonstration, first party
- apiserpent.com/blog/brand-serp-monitoring-2026 Reproducible first-party demonstration, first party
- apiserpent.com/docs Named expert analysis, or a first-party claim about itself, first party
- youtube.com/watch?v=KbDcIYk8TxAwhere AIOTruth found it
What was checked
- canonical URL taken from the page itself
- read the item page (200)
- https://apiserpent.com/docs: supports the item, tier 8 (Named expert analysis, or a first-party claim about itself)
- https://apiserpent.com/blog/geo-vs-aeo-vs-seo-google-ai-search-guide: supports the item, tier 2 (Reproducible first-party demonstration)
- https://apiserpent.com/blog/ai-citation-tracker-python: supports the item, tier 2 (Reproducible first-party demonstration)
- https://apiserpent.com/blog/brand-serp-monitoring-2026: supports the item, tier 2 (Reproducible first-party demonstration)
- https://apiserpent.com/ai-rank-api: supports the item, tier 8 (Named expert analysis, or a first-party claim about itself)
Limitations
- Everything checked traces back to a single domain, so this is one party's account.
- The demonstration was not independently reproduced by AIOTruth.
AIOTruth judgment
The item received a Supported evidence status because the vendor's documentation, product page and several tutorials all describe the same working feature that the video shows. Relevance scores 10 because tracking brand citations inside AI answers is the core subject AIOTruth covers. Usefulness is lower at 6: the demonstration is short, uses a single example keyword and shows little about how to read or act on the results. Evidence scores 8 because the feature is documented and shown in use. That score comes with a limit: every source traces back to one company, and AIOTruth did not reproduce the results. Originality scores 7 because a unified four-engine query is a practical approach, though several tools now track AI visibility.
How this score was calculated
| Dimension | Weight | Score | What produced it |
|---|---|---|---|
| AIO relevance | 30% | 10 | ai visibility, ai search, generative engine optimization, geo, aeo; 4 scope question(s) matched |
| Usefulness | 30% | 6 | 1 artifact(s), 6 actionable marker(s), 0 measured figure(s) |
| Evidence | 25% | 8 | Reproducible first-party demonstration; 5 verified source(s) across 1 domain(s) |
| Originality | 15% | 7 | no original testing found; closest archive match 0.133 |
aioRelevance x 0.30 + usefulness x 0.30 + evidence x 0.25 + originality x 0.15. The rubric is published in full on the Editorial Method page. Scoring is deterministic: the same item scores the same on every run.
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