Reddit AI Tool Mention Monitor

Monitor one AI-tool subreddit at a time and get a ranked queue of complaints, competitor comparisons, buying intent, and switching signals.

Store-ready positioning

Best-fit buyers

• AI product founders / PMs who need early warning on friction, churn risk, and competitor pull
• Support / success leads who want complaint clusters before they turn into ticket spikes
• Growth / research operators who want real comparison language from public AI-tool communities

Core promise

Give me one AI-tool subreddit, one product, and a competitor set — I return the posts most likely to matter for retention, positioning, and conversion.

The business outcome

• catch complaint clusters before they become churn or reputation drag
• see when users openly compare you against Claude, Gemini, Cursor, Windsurf, or Perplexity
• collect real buyer wording from public communities instead of guessing from internal debate

Why this is sellable

• one narrow input domain: one AI-tool subreddit at a time
• one clear buyer job: monitor product friction and competitor pull
• one output shape buyers understand: ranked attention items
• pay-per-event pricing maps cleanly to one attention item = one monetizable event

What it does

• fetches recent posts from arctic-shift.photon-reddit.com
• matches a target tool, aliases, and competitor names inside posts
• samples comments for lightweight confirmation and sentiment context
• classifies each matched post into complaint, purchase_intent, comparison, switching_intent, or general_mention
• scores each item by urgency and commercial relevance
• pushes one dataset item per ranked attention item
• exposes a custom charge event name: attention-item-found

Inputs

Field	Purpose
subreddit	subreddit name without /r/
brand	primary AI tool or product to monitor
aliases	alternative names or short forms for the tool
competitors	alternatives you want compared against
postLimit	number of recent posts to scan
commentsPerPost	comments sampled per matched post
minAttentionScore	drop low-value items below this threshold
maxItems	cap dataset output size
minPostScore	ignore low-score posts
painKeywords	complaint / friction signals
intentKeywords	buying / evaluation signals
comparisonKeywords	side-by-side evaluation signals
switchingKeywords	migration / replacement signals

Output shape

Each dataset item includes fields like:

• brand
• entityName
• entityType
• mentionType
• attentionScore
• urgencyLabel
• confidence
• matchedCompetitors
• score
• commentCount
• title
• url
• commentSnippets
• summary

The run-level summary includes:

• attentionItemCount
• matchedPostCount
• typeCounts
• topEntities
• executiveSummary

Example use cases

Example 1: Chat assistant monitoring

• subreddit: ChatGPT
• brand: ChatGPT
• competitors: Claude, Gemini
• good for: reliability complaints, switching intent, and side-by-side model comparisons

Example 2: Coding-tool monitoring

• subreddit: Cursor
• brand: Cursor
• competitors: Windsurf, Claude Code
• good for: frustration, migration intent, and competitor pull in coding-tool workflows

Real example signals

From a verified sample run on r/ChatGPT:

Signal	Example	Why it matters
Complaint	Image generation is still terrible with chaotic detail	product quality / reliability friction
Purchase intent	Plus subscribers: are you also seeing frequent failures on complex technical work?	pricing-value tension and downgrade risk
Comparison	Bro forgot black holes mentioning Claude and Gemini	direct side-by-side competitor context

Sample scored outcomes from the verified run:

• ChatGPT → complaint → attentionScore 84
• ChatGPT → purchase_intent → attentionScore 78
• Claude / Gemini → comparison → attentionScore 64

Practical Store listing angle

Better positioning:

• Reddit AI tool complaint monitor
• competitor comparison radar for AI product teams
• switching-intent scout for chat apps and coding assistants
• founder attention queue for fast-moving AI startups

Pricing

This Actor is designed around Pay Per Event pricing so buyers pay for returned attention items rather than vague AI output.

Why teams use it

• surfaces the most actionable posts instead of a raw feed dump
• highlights buying, comparison, and switching language buyers actually care about
• works well for manual review, support triage, founder research, and downstream automation

Known limitations

• depends on Arctic Shift availability rather than official Reddit API access
• classification is keyword-based and intentionally lightweight
• best for fast monitoring and triage, not final market research by itself
• noisy subreddits may need threshold and keyword tuning