Understand the real demand around a topic from the perspective of AI chatbot users and tailor it specifically to your own product range: The model uses the SISTRIX MCP Server with ai_topicresearch to retrieve all topics related to a search term, clusters them thematically, and filters out only the clusters with genuine product relevance.
| Time Required | Time Saved | Difficulty | Application |
|---|---|---|---|
| 1–2 hours | 1–2 days of manual research | Medium | Product content & brand relevance |
Benefits
Understand real demand: Shows how AI chatbot users talk about a topic, including search volume per topic.
Targeted filtering for your own product range: Topics without genuine product relevance can be cleanly excluded, such as services or treatments that your own brand doesn’t offer at all.
Recognize brand visibility: Shows in which generic product clusters your own brand is already mentioned as a leading brand.
Ready-made prompt library: Provides matching prompts per topic and per AI model, directly usable for content briefings.
Prioritize editorial resources correctly: Focus on clusters with genuine product relevance instead of the entire, often much broader, topic field.
Prompt
“1. Call ai_topicresearch with view=overview for [Topic] to see the number of topics and the distribution by journey stage/intent.
2. Then retrieve view=list (limit adjusted to the number of topics) for the raw list of all topics with search volume.
3. Cluster the topics thematically and keep only topics related to the core theme.
4. Match the clusters against your own product range: Which clusters fit products that [Brand] actually sells? Consistently filter out anything related to products, treatments, or services that are not offered.
5. Dive deeper into the 2 to 3 highest search-volume topics per remaining cluster using view=topic (topic_label) to get the buyer persona, emotional driver, any mentioned brands, and the prompt library per AI model.”
Process
- Understand the topic space (Overview): ai_topicresearch(view: overview, keyword: [Topic]) shows the total number of topics as well as their distribution by journey stage and intent. This shows at a glance whether users are mostly seeking information or intending to buy directly.
- Pull the topic list and roughly cluster it: From the complete topic list (view: list), thematic clusters are formed. Topics with no relation to the core theme are excluded.
- Filter for your own product range: Clusters unrelated to the products or services actually offered are consistently filtered out. Only the clusters with direct product relevance remain.
- Dive deeper into top topics per cluster: For the highest search-volume topics per remaining cluster, view: topic provides the buyer persona, emotional driver, any mentioned brands, and the complete prompt library per AI model (ChatGPT, AI Mode, Gemini, Copilot, Perplexity, AI Overviews). If the result is satisfactory, it’s worth continuing the prompt output via the SISTRIX MCP Server for the other clusters as well.
- Document the result: Table formats are well suited for prompt research. All clusters, topics, and top prompts can be documented in an Excel spreadsheet with multiple tabs, for example a topic overview, cluster summary, and top prompts per cluster — directly usable for content briefings.
Output / Result
Live example: Filtered analysis for the topic “eyebrows” (DE), tailored to a brand that only sells eyebrow pencil and serum (here: Benefit Cosmetics):
| Cluster | Example Content | Search Volume | Classification |
|---|---|---|---|
| 1. Shaping & Styling | Plucking, threading, waxing, mapping, DIY techniques | ~119,000 | Base content |
| 2. Products & Tools | Eyebrow pencil, gel, pomade, scissors | ~65,700 | Core case (pencil) |
| 3. Growth & Care Ingredients | Serum, castor oil, biotin, peptides | ~39,800 | Core case (serum) |
| 4. Benefit-specific | Gimme Brow, Brow Setter, eyebrow pencil | ~1,050 | Direct brand match |
💡 Tip: Filter consistently by the actual product range, not just by the overarching topic. Large topic blocks that belong to the word field but have no corresponding offering behind them are not relevant for the content strategy: they would only tie up resources and dilute your own focus.
Combinations and Variants
★ Combination: Prompt research from the AI Check. After the prompt research for relevant topics and their clustering, the prompt research based on your own and competitors’ prompt sets from the AI Check can additionally be incorporated to develop an overall picture.
- Variant for other product lines: The approach can be applied one-to-one to other product categories, each with its own cluster set tailored to the product range.
- Competitive variant: The brand mentions from the topic detail view can be explored further as a competitive benchmark in a separate use case.
Stuck? Our support team helps with setting up the SISTRIX MCP Server and answers questions about using it with Claude or ChatGPT. Contact support
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