Your team gets 200 messages a day across email, Slack, and support channels.
Someone asks "what are people asking about most this week?"
You have no idea. You would have to read all 200 messages to find out.
The content is already there. You just need a system that can tell you what it is about.
ENABLER - Topic detection unlocks routing, prioritization, and trend analysis.
Every piece of content that enters your system needs to go somewhere. Support questions need support. Sales inquiries need sales. Technical issues need engineering. But figuring out what something is about requires reading it. At scale, that becomes a full-time job.
Topic detection lets AI read content and assign categories automatically. Not just keywords like 'billing' or 'password' - actual understanding of what the message is really about. A complaint about pricing that never mentions the word 'price' still gets routed correctly.
This is the first step in any intelligent content routing system. Before you can decide what to do with something, you need to know what it is about.
Topic detection analyzes content and assigns it to one or more categories from a defined set. Those categories can be anything relevant to your business - departments, product areas, issue types, request categories.
Modern approaches use AI to understand meaning, not just match keywords. 'I can not log in' and 'the system will not recognize my credentials' both get classified as authentication issues, even though they share no words in common.
Topic detection is how you turn unstructured content into actionable data. Once you know what something is about, you can decide what to do with it.
Any system that routes, prioritizes, or analyzes content needs to first understand what that content is about. Topic detection is the universal first step.
Content comes in. AI analyzes it against your category definitions. Each piece gets labeled with one or more topics plus confidence scores. Downstream systems use those labels to take action.
Click "Next Message" to see different types of content get classified.
"The export button is not working and I was charged twice for my subscription."
Detected two distinct topics. "Export button not working" signals a technical issue. "Charged twice" signals a billing problem. Both flagged for routing.
Look for specific words
Define lists of keywords for each topic. "Billing," "invoice," "payment" all map to the Finance topic. Fast and predictable, but misses anything phrased differently.
Train on labeled examples
Collect examples of each topic and train a model to recognize patterns. The model learns what Finance content "looks like" rather than memorizing specific words.
Describe topics in natural language
Give an LLM your topic definitions in plain English. "Finance topics include questions about billing, payments, refunds, and pricing." The model uses understanding, not memorization.
Your team lead wants to know what topics are trending in incoming messages. Without topic detection, someone would have to read hundreds of messages manually. This flow categorizes everything automatically and surfaces the answer in seconds.
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You have "Billing Questions," "Payment Issues," "Invoice Problems," and "Pricing Concerns" as separate topics. Every billing-related message gets classified into multiple categories with similar confidence scores. Your routing logic cannot decide where to send it.
Instead: Fewer, broader categories that are mutually exclusive. One message, one primary topic.
Someone writes: "The report did not export correctly and I was charged twice for my subscription." That is both a technical issue AND a billing issue. Your system picks one and ignores the other.
Instead: Support multiple topic labels with confidence scores. Route to primary topic but flag secondary topics for follow-up.
Your system has five topic categories. A message comes in that does not fit any of them. The classifier picks the least-bad option and routes it incorrectly. The user gets frustrated waiting for the wrong team to respond.
Instead: Include an "Other/Unknown" category. Set confidence thresholds. Low-confidence classifications go to human review.
You know how to automatically categorize content into subject areas. The natural next step is understanding how to determine what action that content requires.