Zendesk Support Insights & Sentiment Analysis
Fetches Zendesk tickets to build an interactive knowledge graph that visualizes topics, sentiment trends, and product gaps via AI.
ops and IT teamscustomer support teams
What it does
You retrieve Zendesk support tickets by date, status, or keywords to instantly visualize main topics, sentiment trends, and product gaps. The agent then uses AI to generate topical summaries and surface actionable business ideas derived from your customer discourse.
Step by step
- Configure the Zendesk API client with valid credentials and set up a webhook or scheduled trigger to fetch tickets based on date ranges or status filters.
- Implement the code process step to clean raw ticket data, extracting text fields for title and description while normalizing sentiment scores if pre-calculated.
- Define the InfraNodus graph generation parameters by mapping extracted keywords to nodes and co-occurrence frequencies to edge weights before sending the request.
Where the LLM does the work
- Generate concise topical summaries of customer discourse by feeding InfraNodus node clusters into an AI endpoint to interpret thematic connections.
- Identify potential business ideas or product gaps by analyzing sentiment trends and low-frequency but high-sentiment keywords that indicate unmet needs.
Watch out for
InfraNodus graph generation may fail if the input text is too sparse or contains excessive noise, so filter out stop words and ensure minimum token counts before processing.
Slack notification payloads have size restrictions that can truncate long summaries, so cap the output length or send a link to the full report instead of embedding all text.
Tools that fit
Code API Processing data and variables
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Zendesk Support Insights & Sentiment Analysis: Fetches Zendesk tickets to build an interactive knowledge graph that visualizes topics, sentiment trends, and product gaps via AI. ## Specification - What it does: You retrieve Zendesk support tickets by date, status, or keywords to instantly visualize main topics, sentiment trends, and product gaps. The agent then uses AI to generate topical summaries and surface actionable business ideas derived from your customer discourse. - Trigger: On a schedule (Manual · daily scheduled) - Autonomy: You stay in control - Expected setup effort: under an hour - Tools/services involved: - Zendesk: Fetching support ticket data - InfraNodus: Generating knowledge graphs and summaries - Slack: Sending notification alerts - Code: Processing data and variables ## Known pitfalls, handle each one explicitly in your implementation 1. InfraNodus graph generation may fail if the input text is too sparse or contains excessive noise, so filter out stop words and ensure minimum token counts before processing. 2. Slack notification payloads have size restrictions that can truncate long summaries, so cap the output length or send a link to the full report instead of embedding all text. ## Reference implementation https://n8n.io/workflows/4688 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/zendesk-support-insights-sentiment-analysis/ via usecasesforagents.com
Frequently asked questions
Can I use a different Support & Ticketing than Zendesk?
Yes. Zendesk is only the example support & ticketing in this recipe. The same flow works with Hiver, Intercom, Marker.io and Syncro. Swap the support & ticketing connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for Ops-IT teams who need one more recurring task off a human's plate.
Support teams facing the same queue pressure can run this exact pattern on their tickets.