AI Social Listening Dashboard — Monitor What Your Market Is Saying Before Your Competitors Do
Built by CatalystHelix ·
About this build
A Claude and Perplexity powered workflow that monitors Reddit, Twitter/X, LinkedIn and niche forums daily for mentions of your brand, competitors and target keywords — summarises the most important signals into a structured morning briefing with sentiment analysis, emerging trends and recommended actions delivered to y
What was built
Built this after realising I was completely flying blind on what people were saying about my product and my competitors. I would occasionally search Reddit or check Twitter but it was random and inconsistent. By the time I found a thread where someone was comparing me unfavourably to a competitor the conversation was already over and the moment to join it had passed. The workflow runs every morning at 6am before the day starts. Here is exactly what it does: Step 1 — Daily search sweep via Perplexity n8n triggers a series of Perplexity searches across three categories every morning. Brand mentions — any mention of the product name, company name or founder name across Reddit, Twitter/X, LinkedIn, Hacker News and relevant niche forums from the last 24 hours. Competitor mentions — the same search pattern applied to each of the top 3 competitors. Looking for what people are saying about them, what problems they are complaining about and what they are praising. Keyword trends — searches for 5 to 10 keywords most relevant to the product category to identify what questions, complaints and conversations are emerging in the target market right now that might not mention any specific brand. Step 2 — Signal extraction and classification via Claude All raw Perplexity results get passed to Claude with a structured prompt that does four things simultaneously. Filters out irrelevant noise — mentions that are clearly unrelated to the business context get removed before anything else. Classifies each remaining mention as positive, negative or neutral with a one sentence explanation of why. Identifies any emerging themes appearing across multiple mentions rather than just one-off comments. A single negative comment is noise. Three negative comments about the same issue in the same week is a signal. Flags any mentions that likely require a direct response — an unanswered customer question, a complaint that has gathered engagement, or a conversation where joining in would be genuinely useful rather than self-promotional. Step 3 — Trend logging in Airtable Each day's top themes and sentiment scores get written to an Airtable base. This builds a running record over time so you can see whether sentiment is improving or declining week over week, whether a particular platform is generating disproportionate negative mentions and whether competitor mentions are accelerating in a way that suggests a new campaign or product launch. Step 4 — Morning briefing delivery By 8am a structured briefing lands in either Gmail or Slack containing five sections. Overall sentiment summary for the last 24 hours. The 3 most important mentions that need attention today. Any emerging trend worth watching. One competitor insight from the previous day. A suggested action based on what was found. The whole thing runs without touching it. The market intelligence is just there when the day starts. What took the longest to get right: The noise filtering was the hardest part. Early versions were surfacing completely irrelevant mentions because the brand name shared words with unrelated topics. Fixed by adding context qualifiers to every search query — always combining the brand name with the product category and at least one additional qualifier that narrows the context. The briefing length went through 4 iterations. The first version was too long — nobody reads a 600 word daily briefing consistently. The final format has 5 sections each under 3 sentences. Short enough to read in 90 seconds, specific enough to know immediately whether anything requires action. The competitor mention searches were initially too broad and kept returning results for companies with similar names in unrelated industries. Fixed by using the competitor name combined with their primary product name and their most distinctive feature in every search query — three identifiers together are far more precise than a company name alone. Results after 10 weeks: Caught a thread on Reddit where a user was comparing the product unfavourably to a competitor based on outdated information from 8 months ago — was able to join the conversation within the same day with accurate updated information. Without this build the thread would have been found 3 weeks later when someone forwarded it. The weekly Airtable data showed Reddit was generating 3 times more negative mentions than Twitter/X despite Twitter having higher total mention volume. That single insight completely changed where community engagement time was being invested. Spotted an emerging feature request across 5 separate Reddit threads over a 2 week period — surfaced it to the product team 4 weeks before it started appearing in the official support inbox. Will share the full n8n workflow JSON and Claude classification prompt in comments for anyone who wants to deploy this.