Detection guide
Keyword alerts vs. AI intent detection
Keyword alerts answer “did this text contain a term?” Intent detection answers “does this person appear to own a problem our product can solve?” Use exact matching for known language and semantic review when the same need can be expressed many ways.
- Published by
- Intoru
- Published
- July 16, 2026
- Last reviewed
- July 16, 2026
- Maintenance owner
- Intoru product team
Where keyword alerts win
Exact terms are fast to understand, easy to audit, and inexpensive to run. They work especially well for brand names, domains, product names, uncommon competitor names, error codes, and phrases customers repeat consistently.
- “intoru.ai” for a domain mention.
- “F5Bot” for a specific competitor mention.
- “SOC 2 evidence” for a narrow operational phrase.
- An exact error message that signals a known technical problem.
The failure mode is noise. A common word such as “leads,” “users,” or “analytics” appears in many threads with no buyer, no problem ownership, and no fit.
Where intent detection helps
Buyers often describe the job without using your category. A founder might ask how to find interview participants without ever saying “user research recruitment.” A finance operator might describe hours spent reconciling payouts without naming “reconciliation software.”
Contextual classification can connect those descriptions to a campaign and reject obvious mismatches such as vendors selling the same service, students asking for homework help, job seekers, or broad debates. Results still need traceable evidence and human review because model judgments can be wrong.
The strongest setup is often hybrid
Use exact terms to capture high-confidence brand and competitor events. Use semantic retrieval to widen recall around the customer’s job. Then apply a contextual gate that checks problem ownership, ideal-customer fit, disqualifiers, and whether the post is a buyer request or a vendor promotion.
A practical decision rule
- Start with exact alerts when five to twenty specific terms cover the important events.
- Add include, exclude, subreddit, author, and content-type filters before adding a model.
- Add contextual detection when relevant posts routinely omit your terms or when literal matches remain too noisy.
- Review false positives and false negatives against the same campaign definition. Do not judge quality by alert volume.
How to evaluate either approach
Label a representative sample before changing the setup. Measure how many real opportunities were found, how many irrelevant alerts reached a human, and which good posts were missed. Keep buyer intent, product fit, community rules, and reply quality as separate checks. A matching post can still be a bad place to promote.
For the engagement step, use the subreddit rules directory and read the live thread before replying.