ANALYZE
AI 검색 순위 분석

It's a technique for discovering uncontested opportunity areas.

• Gap: Keywords or intents that competitors occupy but our brand is missing.

• Empty Node: Topics with high importance on the network graph that no competitor has yet claimed.

Team HAI's Gap Visualizer visualizes these to present low-cost, high-efficiency attack points.

It quantifies the nuance and role when AI mentions our brand.

Rather than just being mentioned (Recall), high scores (100/80 points) are awarded when cited in positive contexts like 'Representative' or 'Recommended'. Conversely, comparison targets or negative examples receive lower scores.

Whether they can explain the cause of performance.

• Vanity Metrics: Simple exposure counts, click counts. They are merely results and don't explain 'why it increased', so they don't help with strategy.

• Causal Metrics: Team HAI provides Coverage (territory size), Differentiation (uniqueness), Alignment (relevance), etc. These metrics are the causes (X) that create results, so improving them improves outcomes (Y).

They're safeguards for anti-abuse filtering and sustainable growth.

If you rapidly flood content to boost rankings, the vector centroid shifts drastically. AI may recognize this as spam or noise and purge it from memory. Team HAI monitors vector changes over time (t1 to t2) to encourage gradual, stable authority building.

Yes, absolutely. Semantic analysis in vector space is language-agnostic. Team HAI operates on a global database, so it equally supports competitor analysis in overseas markets, understanding local customer search intent, and designing structures optimized for AI in those language regions.