Organic community management requires sifting thousands of posts manually. AI scoring surfaces the 1% worth engaging with — and pre-drafts replies that a human approves in 30 seconds.
10x
signal-to-noise
vs. manual monitoring
30 min
check cadence
across all platforms
3
scoring axes
per post
0
auto-published
always human-approved
Typical build: 2–3 week sprint · Fixed price · Zero delivery risk
Monitor cadence
Every 30 min
Scoring axes
3 per post
Signal ratio
10x improvement
A community manager manually monitoring Reddit and LinkedIn reads thousands of posts per week to find the handful where a thoughtful reply could generate leads. That is 80–90% of their week spent filtering noise — not creating value.
A genuinely helpful reply in the right subreddit generates more trust than a $50 CPM ad. The problem is not the channel — it's the scale. AI scoring makes the channel viable without a team of 5 community managers.
Manual community teams naturally gravitate toward the most upvoted posts — which are the most competitive. The highest-value engagement opportunities are buried in second-page posts with 12 upvotes from someone actively comparing solutions.
This is the actual workflow Kovil AI engineers can build and deploy — not a diagram. Here is what runs inside every node.
Custom Python scripts connect to the Reddit API and LinkedIn API, monitoring a configured list of subreddits, LinkedIn groups, and niche industry forums for posts containing client-specific keywords, competitor names, and problem-statement phrases. The monitor runs every 30 minutes via n8n scheduler. New posts are deduplicated against a rolling 7-day history stored in Airtable — each post is only processed once. Platform-specific rate limits are respected automatically.
Claude 3.5 Sonnet receives each new post and scores it on three axes: (1) Relevance to client ICP (0–10): does the poster match the target customer profile based on their history, flair, and post content? (2) Engagement potential (0–10): is this a high-visibility post likely to be seen by many users? (3) Purchase intent signal (0–10): does the post suggest the person is actively considering a solution? Each score is returned as structured JSON with a one-sentence rationale — enabling the human reviewer to understand why a post was elevated.
n8n evaluates the combined score from Claude. Posts scoring above the configurable threshold (default: 21/30) are written to the client's Airtable monitoring dashboard. The threshold is client-configurable — a highly selective client might set 24/30; a high-volume engagement strategy might use 18/30. All posts are stored regardless of score for reporting purposes, but only high-scorers appear in the active review queue. The dashboard shows post URL, platform, score breakdown, post content, and the Claude-generated rationale.
For each post that clears the threshold, Claude 3.5 Sonnet drafts a contextual reply. The reply is constrained by the client's community guidelines playbook (stored in a Notion doc): specific topics to avoid, claims that require substantiation, approved and blocked phrases, and a tone description. Replies are helpful first — not promotional. Claude is explicitly instructed to avoid mentioning the client by name unless the post directly asks for a recommendation, and even then to frame it naturally within the context of the conversation.
The account manager opens the Airtable dashboard and sees a queue of high-signal posts, each with: the original post content, Claude's score breakdown, the drafted reply, and one-click approve/edit/skip actions. The typical review takes 30–60 seconds per post. The operator can approve the reply as-is, make quick edits directly in the Airtable cell, or skip the post entirely. Approved replies are sent to a publish queue — never auto-published without human sign-off. This maintains community authenticity and prevents brand risk.
After publish, n8n logs the complete engagement record to Airtable: post URL, platform, original post content, published reply text, all three Claude scores, publish timestamp, and the human reviewer who approved it. A weekly Slack digest is posted every Monday: total posts monitored, high-signal posts identified, replies published, and any notable engagements (posts that generated significant response). This creates a growing record of community engagement that informs future content strategy.
Community monitoring
Monitors configured subreddits for keyword matches every 30 minutes. Returns post content, engagement metrics, and poster history for scoring.
Scraping + processing
Custom scripts handle Reddit pagination, LinkedIn API calls, deduplication logic, and niche forum monitoring where APIs are unavailable.
Scoring + drafting
Scores posts on 3 axes with rationale. Drafts contextual replies following client community guidelines. Returns structured JSON output.
Orchestration
Runs 30-minute monitoring scheduler, routes posts to Claude for scoring, manages threshold filtering, and coordinates publish workflow.
Dashboard + logging
Hosts the human review dashboard. Stores all posts, scores, drafts, and published replies with full outcome tracking.
Weekly digest
Posts Monday digest: posts monitored, high-signal identified, replies published. Immediate alerts for exceptionally high-scoring posts.
Kovil AI engineers scope, build, test and deploy this monitoring system end-to-end. Your community manager spends 30 minutes approving replies, not 8 hours reading posts.
The workflow uses the official Reddit API, not unauthorised scraping. Reddit's API provides programmatic access to public post data, which is permitted for monitoring and analytics purposes within their rate limits. The workflow operates within Reddit's API usage policies and does not scrape private communities or bypass access controls.
Claude 3.5 Sonnet receives each post with a structured scoring prompt specifying: ICP relevance (0–10, how closely the poster matches the client's ideal customer profile), engagement potential (0–10, likelihood that a helpful reply would generate meaningful interaction), and purchase intent signal (0–10, strength of buying signals in the language). Scores are returned as JSON with a one-sentence rationale per axis.
Account managers access a filtered Airtable view showing only posts above the relevance threshold. Each row shows the original post, subreddit, score breakdown, and Claude's drafted reply. The AM reads the draft, edits it inline if needed, and clicks Approve. n8n receives the approval webhook and publishes the reply to the correct platform via the relevant API.
The standard build monitors Reddit and LinkedIn groups. Additional integrations can be added for Quora, niche industry forums (via RSS scraping), Facebook Groups (via Meta API where permitted), and Twitter/X search via their API. Each platform is a separate n8n trigger node feeding the same Claude scoring pipeline.
Book a 30-minute discovery call. Kovil AI engineers will scope the subreddit configuration, Claude scoring criteria, and Airtable dashboard for your specific community engagement strategy — fixed price, zero delivery risk.
Typical sprint: 2–3 weeks · Fixed-price · Fully managed delivery · Post-launch support included