AI Scoring & Tagging

Skip the noise. Reach the buyers worth responding to

Every mention is scored for buying intent before it reaches your feed. You see the people ready to buy, not the job posts, spam, and tutorials that clog keyword alerts.

RedditXLinkedInBluesky
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Spent 5 minutes on SnitchFeed, dropped a comment → new user
Abhishek Chakravarty
Abhishek Chakravarty
Co-founder, Youform

The problem

Keyword monitoring finds every match. Most of them are junk.

Set up a keyword alert for your category and your feed fills with noise before a single real buyer appears. This is the default state of every keyword-only social monitoring tool.

Raw keyword feed
  • We're hiring a social media manager with CRM experience
  • That checkout flow feels like a CRM designed in 2009
  • New blog post: 10 CRM trends to watch this year [VENDOR]
  • CRM certification exam tips and study resources
  • Anyone know a lightweight CRM for a 3-person SDR team?
  • CRM tutorial: how to set up your pipeline in 10 minutes

1 signal buried in 6 results. Multiply by 100+ daily keyword hits.

SnitchFeed AI-scored feed
  • Anyone know a lightweight CRM for a 3-person SDR team?
    Score 94Buy IntentNeutral
  • Fed up with Salesforce pricing. Looking for something simpler for a startup.
    Score 91Competitor MentionNegative
  • We just switched CRMs and honestly the migration was the easy part. Happy to share what we learned.
    Score 87Customer TestimonialPositive

3 high-confidence signals. Nothing else in your feed today.

How it works

From raw keyword hit to scored signal

AI scoring runs automatically on every mention. No configuration required. Here is what happens between a post going live and it appearing in your feed.

  1. 01

    A mention is captured

    SnitchFeed detects a post or comment containing your keyword across Reddit, LinkedIn, X, or Bluesky. At this point it is a raw match: it has not been evaluated for quality.

  2. 02

    AI reads brand context, intent signals, and post content

    The AI cross-references three inputs: your brand context (what you care about and who your buyers are), the author's signal intent (are they asking, complaining, recommending, or hiring?), and the full post content including thread and tone. All three combine into a single relevance decision.

  3. 03

    Scores and tags are assigned to the mention

    The mention is stamped with a relevance score (0-100), a buying intent level (high, medium, or low), and a sentiment score (positive, negative, or neutral). AI category tags are added in the same pass.

  4. 04

    Your GTM stack gets notified

    High-scoring mentions trigger an alert to whichever channels you have configured: Slack, email, Discord, or webhook. Your team sees the score, intent level, and category tag before they click through.

  5. 05

    Low-scoring mentions are filtered, not deleted

    Mentions below your threshold are kept out of your primary feed but stored in full. You can audit the unfiltered view at any time and adjust the threshold if the AI is being too aggressive or too permissive.

What gets scored

Three dimensions, one complete picture

Every mention is evaluated on relevance, buying intent, and sentiment simultaneously. Each score answers a different question about whether and how to respond.

Relevance

Is this post about your category?

Context, not keyword count. A mention of "CRM" in a complaint about a checkout flow scores near zero. The same word in "we need a lightweight CRM for our SDR team" scores high.

Buying Intent

Are they in-market right now?

Passive references score low. Active signals score high: "thinking about switching", "frustrated with Y", "anyone recommend a tool for Z". Those are the posts worth your time.

Sentiment

How do you open the reply?

Positive, negative, or neutral, tagged before you click through. A frustrated post needs a different opening than a curious one. Know the tone before you respond.

What gets removed

The five categories of noise SnitchFeed removes automatically

AI scoring does not just rank mentions. It identifies and removes entire categories of irrelevant content before they enter your workflow. These are the most common sources of false positives in keyword-based social monitoring.

  • Job postings

    "We're hiring a social media manager with CRM experience"

  • Vendor promotions

    Posts from competitors pitching their own tools

  • Tangential keyword references

    Your keyword appears incidentally, unrelated to your category

  • Spam and recycled content

    Syndicated posts, bots, duplicate shares

  • Off-language content

    Posts in languages outside your target market

What remains in your feed

High-relevance posts where real buyers discuss their needs, frustrations, and tool evaluations.

See what AI scoring surfaces for your keywords.

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Side by side

AI scoring vs. keyword-only monitoring

Factor
Keyword-only monitoring
SnitchFeed AI scoringBetter
Noise in your feed
High: 70-90% of raw keyword hits are irrelevant
Low: filtered before it reaches you
Time spent triaging
1-2 hours per day
Under 5 minutes
Sentiment tracking
Manual or none
Automatic on every mention
Buying intent detection
None
High, medium, or low on every mention
Response speed
Hours, after manual triage
Minutes: alerted instantly on high-score matches
Platform coverage
Usually 1-2 platforms, manually checked
Reddit, LinkedIn, X, Bluesky: all scored uniformly

Who it is for

Three ways teams use AI relevance scoring

Founder-led sales

5 minutes each morning, only real signals

  • Raw feed: hundreds of daily hits including job posts and promotions
  • AI-scored feed: only people actively evaluating a tool like yours
  • No sales team needed to keep up

Typical result: 200 raw hits reduced to 8-12 high-intent matches per day.

GTM teams tracking competitors

See frustration, not fan posts

  • Raw monitoring: marketing posts, job listings, satisfied customer tweets
  • AI scoring: only frustrated, switching, or alternatives-seeking posts
  • Those are the conversations worth entering

Intents surface competitor mentions where someone is unhappy or shopping for an alternative.

Product teams collecting feedback

Unfiltered sentiment, not survey bias

  • Filters passing references and off-topic noise automatically
  • Surfaces posts where your category is discussed critically and organically
  • What remains is unsolicited, unbiased product signal

Tag by sentiment to separate feature requests from bug complaints from praise.

What teams use this for

FAQ

Common questions about AI scoring

Still have questions? Reach out at hello@snitchfeed.com

Still have questions? Reach out at hello@snitchfeed.com

Stop reading noise. Start responding to buyers.

SnitchFeed scores every mention automatically. Set up takes 5 minutes. First results appear in 60 seconds.

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