techhorizonlabs Brand Mention Scanner Skill
Core Insight
Brand mentions correlate more strongly with AI visibility than traditional backlinks. An Ahrefs
brand study (2025, ~75,000 brands, cited as reported — see docs/SOURCES.md)
found unlinked brand mentions — references to a brand name with no hyperlink — predict whether
AI systems cite and recommend a brand better than Domain Rating or backlink count.
This measures off-page authority signals, not answer-engine outcomes. Wikipedia/Wikidata are checked live via their APIs; the other platforms are assessed via search, not by querying the AI engines. A high Brand Authority Score means the signals AI trusts are present — it does not confirm any engine actually names you. For that live check, run the free scan at areyoufoundbyai.com (two buyer questions on ChatGPT and Gemini; the paid measure covers all seven engines).
The critical finding: the platform the mention sits on matters enormously. A mention on YouTube or Reddit carries far more weight for AI citation than one on a low-authority blog, because AI training data and retrieval systems disproportionately index high-engagement platforms.
This inverts a core SEO assumption. In SEO, a backlink from a high-DR site is the gold standard. In GEO, an unlinked mention on Reddit or in a YouTube description may be worth more than a dofollow backlink from a DR 70 blog.
Platforms that matter
AI systems weight a handful of platforms far above backlinks. Each platform's rationale, scan
recipe, and 0–100 scoring rubric live in references/platforms.md —
read it before scoring. Ranked by correlation with AI citation:
- YouTube (~0.737, strongest) — channel + third-party video/description/transcript mentions
- Reddit — subreddit discussion, recommendation threads, sentiment
- Wikipedia / Wikidata — the entity-recognition foundation
- LinkedIn — professional / B2B authority signals
- Other — Quora, Stack Overflow, GitHub, forums, news, podcasts (scored as one basket)
Composite Brand Authority Score
Score each platform 0–100 (rubrics in references/platforms.md), then weight:
| Platform | Weight | Rationale |
|---|---|---|
| YouTube Presence | 25% | Strongest correlation with AI citation (~0.737) |
| Reddit Presence | 25% | Second strongest; critical for product recommendations |
| Wikipedia / Wikidata | 20% | Entity-recognition foundation; AI training-data cornerstone |
| LinkedIn Authority | 15% | Professional authority signals; B2B relevance |
| Other Platforms | 15% | Supplementary signals (Quora, GitHub, news, forums, podcasts) |
Brand_Authority_Score = (YouTube * 0.25) + (Reddit * 0.25) + (Wikipedia * 0.20) + (LinkedIn * 0.15) + (Other * 0.15)
| Score | Rating | Interpretation |
|---|---|---|
| 85-100 | Dominant | Well-recognized entity across AI platforms. Highly likely to be cited and recommended. |
| 70-84 | Strong | Solid cross-platform presence. AI systems likely recognize and cite it for relevant queries. |
| 50-69 | Moderate | Present on some platforms but with gaps. AI citation is inconsistent. |
| 30-49 | Weak | Limited presence. AI systems may not recognize it as a distinct entity. |
| 0-29 | Minimal | Negligible presence. AI systems are unlikely to cite or recommend it. |
Analysis Procedure
Step 1 — Identify the brand
Gather from the user or the website: exact brand name (and official variants), founder/CEO name(s), domain, industry, top 3 products/services, and key competitors (for comparison context).
Step 2 — Scan each platform
Work through every platform using the scan recipes in
references/platforms.md, and score each 0–100 against its rubric there.
Wikipedia is the one trap: web search alone produces false negatives. Run the Python API check in
references/platforms.mdfirst — if the API says a page exists, it exists; never override that with a failed search result.
Step 3 — Assess sentiment
For Reddit and other discussion platforms, judge sentiment from the most recent and most prominent mentions:
| Sentiment | Indicators |
|---|---|
| Positive | Recommendations ("I love [brand]", "we switched to [brand]", "highly recommend"), upvoted mentions, favourable comparisons |
| Neutral | Factual mentions ("we use [brand] for…", "[brand] offers…"), questions, balanced comparisons |
| Negative | Complaints ("avoid [brand]", "terrible support"), downvoted recommendations, unfavourable comparisons |
| Mixed | Both — note the ratio and the primary themes |
Step 4 — Competitive comparison (optional)
If competitors are known, quick-scan their platform presence for context. It calibrates the score: "moderate" Reddit presence in an industry where competitors have none is relatively strong.
Step 5 — Calculate and recommend
- Score each platform 0–100 using the rubrics.
- Apply the weights for the composite Brand Authority Score.
- Identify the strongest and weakest platforms.
- Turn the weakest platforms into specific actions using the presence-building tips in
references/research.md.
Output
Provenance (THL): tag the score
[scan](data fetched this run),[partial-scan],[heuristic](judgement, no data), or[unmeasured]— and emit—instead of a number when[unmeasured]or pure[heuristic]. A number with weak provenance still reads as hard data. See the GEO Method.
Write GEO-BRAND-MENTIONS.md using the template in
references/output-template.md — score header, platform
breakdown table, per-platform detail, tiered recommendations, competitive context, and a
one-line key takeaway. Fill every placeholder or mark it N/A.
Reference data
Correlation strengths (the "why YouTube/Reddit beat backlinks" evidence) and the per-platform
presence-building playbook are in references/research.md.