Reddit AI Tool Mention Monitor

An Apify-ready Python Actor that scans one AI-tool subreddit for a target product plus competitors, then returns a ranked attention queue of complaints, comparisons, purchase intent, and switching signals.

Store-ready positioning

Who this is for

• founders and PMs tracking product friction, churn risk, and competitor pull
• support and success teams that want complaint spikes before they become ticket volume
• growth and research operators harvesting real comparison language from 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.

Buyer personas

• Founder / PM: wants fast signal on reliability complaints, feature frustration, pricing pain, and competitor pull
• Support lead: wants recurring bug and outage patterns before customer frustration compounds
• Growth / research operator: wants side-by-side language and switching intent to improve copy, sales research, and launch messaging

ROI angle

• 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 more sellable than a generic AI wrapper

• one narrow input domain: one AI-tool subreddit at a time
• one clear use case: product and competitor monitoring for AI tools
• 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

Practical Store listing angle

Better positioning:

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

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

Pricing

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

Example use cases

Example 1: chat assistant monitoring

• subreddit: ChatGPT
• brand: ChatGPT
• competitors: Claude, Gemini
• good for: tracking 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: spotting frustration, migration intent, and competitor pull in coding-tool workflows

What makes it useful

• 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