Brand Monitoring Workflow
Someone tweets about you at 6pm on a Saturday. By 6:07pm the reply is drafted in your voice, waiting for your one-tap approval. Everything else gets logged.
A founder tweets that your product saved them 6 hours. You see it 4 days later. A Reddit thread accuses your pricing of being misleading and has 89 upvotes before you notice. A podcast mentions you positively in episode 47 — you never hear about it because nobody transcribes podcasts. Google Alerts catches maybe 20% of what's actually happening, mostly the SEO-junk mentions, and nothing from Discord, Slack communities, or LLM answers.
The AI CMO and AI Head of Growth run always-on monitoring across the full mention surface: Twitter, Reddit, Hacker News, LinkedIn, YouTube transcripts, podcast transcripts, review sites, Discord/Slack communities, and LLM answers (ChatGPT, Perplexity, Claude). Every mention gets sentiment-tagged, impact-scored, and routed — high-impact positives get amplified, negatives get addressed, LLM citation gaps get a new piece of content.
How it runs
- 1Multi-surface listening
Tycoon runs watchers on Mention, BrandMentions, Talkwalker, Google Alerts, and native Twitter/Reddit APIs for real-time coverage. For podcasts it uses Podscan or Listen Notes transcript APIs. For LLM citations it runs a weekly probe (via Profound-style queries) to see when and how your brand appears in ChatGPT/Perplexity/Claude answers. Every mention lands in one unified stream.
- 2Sentiment and impact scoring
AI CMO classifies each mention: sentiment (positive/negative/neutral/complaint/question), reach (follower count, subreddit size, podcast audience, domain authority), and relevance (is this actually about your product, or coincidental name collision?). High-reach + high-sentiment-delta items float to the top.
- 3Route by mention type
Positive high-reach mention → queue for amplification (retweet, quote-tweet with thanks, or community highlight). Negative mention → route to AI Customer Support for response drafting. Question mention → route to answer (drafted by AI Head of Growth with link to docs). Bug complaint → feed to bug-triage workflow. Competitor-comparison mention → route to AI CMO for positioning response.
- 4Draft responses in your voice
For mentions requiring a reply, AI Customer Support or AI CMO drafts it in your voice (trained on your past public replies). Tone-matched, specific (references the exact complaint), and short. Drafts land in chat for one-tap approve+send, or escalate to you if the situation is unusual (executive trash-talk, viral thread). Nothing auto-sends publicly without your approval.
- 5LLM citation audit
Every Wednesday the AI Head of Growth queries a panel of prompts relevant to your product — 'best tool for X', '[competitor] alternatives', 'how to do Y' — across ChatGPT, Perplexity, Claude, and Gemini. Records whether you were cited, what URL was the source, and how your positioning was described. Gaps (you're not cited where you should be) become content briefs routed to the AI CMO.
- 6Weekly pulse report
Monday morning, AI CMO publishes the brand report: mention volume (up/down), sentiment breakdown, top 3 positive mentions (with screenshots), top 3 negative/risky mentions with status, LLM citation share vs competitors, share-of-voice trend line. One scroll, everything you need.
- 7Viral spike response
If a mention spikes — a tweet gaining 500+ likes in an hour, a Reddit thread breaking 50 upvotes, a Hacker News post hitting the front page — the AI pages you within 5 minutes regardless of topic, drafts a response plan (engage / clarify / ignore), and keeps refreshing evidence as the thread grows. You decide engage or not; the AI has the context ready.
Who runs it
What you get
- ✓Never miss a positive mention — every one logged and 60%+ amplified
- ✓Negative mentions addressed within the hour, not the week
- ✓LLM citation share tracked weekly — you know where AI recommends you vs doesn't
- ✓Share-of-voice trend line visible week over week
- ✓Viral spikes get a response plan before the thread peaks
- ✓Founder's public voice stays consistent — AI drafts match prior replies
- ✓Podcast and YouTube mentions captured via transcripts, not just text platforms
Frequently asked questions
Most brand monitoring tools have terrible signal-to-noise. How is this different?
Generic brand monitoring surfaces every mention and leaves prioritization to you — which means you spend 20 minutes a morning scrolling and acting on maybe 2 items. Tycoon's AI CMO does the prioritization up front: a mention from an account with 180 followers in a subreddit that's basically dead gets logged but not escalated; a mention from a founder with 50K followers in a relevant pro community gets flagged with a draft reply within 10 minutes. The ratio of 'things I look at' to 'things that matter' goes from 1:20 to roughly 1:1. Noise is still captured for search history — you can look up any mention later — but it doesn't interrupt your day.
How do you handle brands with ambiguous names — like if my product is called 'Tycoon' and there's also a game called 'Tycoon'?
Name disambiguation is the first classification step. The AI uses context clues: words in the mention (SaaS / AI / game / MMO), the surrounding thread topic, the poster's bio and history, and your product's taxonomy. For highly ambiguous names it maintains an exclusion filter (ignore mentions of your name when paired with 'MMO' or 'Xbox'), tunes it continuously based on your overrides, and errs toward false positives rather than false negatives (it's cheaper to ignore a false hit than miss a real one). Over ~2 weeks of training on your corrections, precision converges to ~95% on normal brand names and ~85% for especially ambiguous ones. For very generic brand names (e.g. 'Launch', 'Pitch') you may need tighter filters specified up front.
Can it actually monitor LLM citations? ChatGPT answers change every time you ask.
LLM citation is sampled, not exhaustive. The AI Head of Growth runs a fixed prompt panel — say, 50 prompts relevant to your product — across each major LLM (ChatGPT, Perplexity, Claude, Gemini) every week. Each prompt runs 3-5 times to average out variance. Share-of-voice is computed as (# of prompts where you're cited) / (# of prompts total). This gives you a trend line: week 1 you were cited in 14/50 prompts, week 8 you're cited in 27/50. Competitor SOV runs on the same prompts. It's not real-time alerting on LLM mentions (that's not possible with any tool), but it is a reliable longitudinal signal of your AI visibility, which is increasingly the main distribution channel for B2B SaaS.
What about private communities — Slack, Discord, private podcasts, etc? I'm more worried about a Slack community roast than a Twitter one.
Public monitoring can't reach those, but you can bridge them in two ways. One: join the communities as yourself (or have your AI Customer Support join as a clearly-labeled AI team member in communities that allow it), and Tycoon ingests the member's relevant messages. Two: customers who want to report something from a private community can forward the message to a dedicated inbox (brand-alerts@yourco.com) and the AI triages it the same way. Neither gives you full coverage of private spaces, but in practice the private-community mentions that matter tend to get surfaced to you eventually through one of these paths. The AI is honest that private-community coverage is incomplete and flags it in the weekly report.
How does this avoid auto-replying to a tweet and making the AI-voice problem worse?
Auto-send to public platforms is off by default and not configurable on Twitter/Reddit/HN. Every public reply requires your one-tap approval. The AI drafts, labels the reply type ('response to complaint', 'thank-you to advocate', 'clarification to misinformation'), attaches the original context, and waits. What auto-sends: private channels where you've opted in (Intercom ack to a bug report, DM response to a product question from someone already in conversation with support). Public posts always go through you. This costs some latency on weekends when you're slow to approve, but it keeps the brand voice unmistakably human-run. A single bad public AI reply damages trust more than a dozen good ones help it.
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