So, let me give you the exact system they used.
A few weeks ago I saw a post that made me stop scrolling. And I instantly saved it in my bookmarks. I read the same post again at X and Reddit. And realized I should actually make notes from the same and start applying it to BoutPredict.
A founder opened Stripe and thought there was a bug.
$1K MRR.
Almost none of it from Google.
The traffic was coming from AI
ChatGPT recommending their product, Perplexity citing their content, Claude naming them when someone asked for a tool recommendation.
I’ve been building BoutPredict for a while now, and getting AI referral traffic has been something I’ve thought about but never systematically pursued.
Reading this breakdown changes that.
Let’s walk through exactly what this founder did, why it works, and what I’m personally taking from it.
Why this is actually happening
Two numbers matter here.
AI Overviews from Google cut click-through rates on top-ranking organic results by roughly 58% in 2025, according to Ahrefs data.
At the same time, ChatGPT crossed 900 million weekly active users sending around 2 billion prompts per day.
People aren’t Googling products anymore.
They’re asking AI to recommend them.
And here’s the part that surprised me:
traffic referred from AI tools converts at roughly 4–5x the rate of traditional organic search.
The reason makes sense when you think about it.
Someone who clicked your link from a Perplexity answer already received a mini pitch from the AI before they landed on your page.
They arrive pre-qualified.
The shift is from ranking to being cited.
The SEO world calls this AEO(Answer Engine Optimization), or GEO (Generative Engine Optimization).
The terminology isn’t settled yet but the mechanic is simple:
You optimize so that when someone asks an AI “what tool does X,” that AI names you.
What the founder built
They run a product called ReddGrow which is like a Reddit intelligence platform for founders and growth teams.
The idea is to surface relevant conversations and participate in them without being spammy about it.
They weren’t chasing AEO deliberately.
They were just doing Reddit marketing properly.
Then they noticed something in their analytics:
signups were landing on pages with no Google ranking, no backlinks, no ad spend.
The referrer string was chatgpt.com or perplexity.ai.
They’d been accidentally training AI engines to cite them.
The mechanism: ChatGPT and Perplexity pull heavily from Reddit as a trusted source.
Real people, real conversations, real use cases.
When you post genuinely useful content into the right subreddits, you’re contributing to the exact corpus these engines draw from when generating answers.
68% of AI-generated answers cite Reddit.
That’s not a small number.
The system they built
Once they understood what was happening, they built a repeatable workflow around it.
Three tools:
Claude as the reasoning layer,
ReddGrow as the Reddit data layer,
and Postiz for cross-platform distribution.
Steps!
Step 1: Wire Claude to live Reddit data
They connected Claude to ReddGrow via MCP, which lets Claude access external tools directly.
The setup for Claude is just adding a custom connector pointing to https://mcp.reddgrow.ai/mcp and authenticating via OAuth.
Once connected, Claude can pull live subreddit data, check community rules, review campaign state, and flag drafts before posting — all without leaving the conversation.
Before touching any subreddit,
they had Claude pull community rules and activity data:
reddgrow reddit subreddits about subredditname
reddgrow reddit subreddits rules subredditnameThis sounds like a small step.
It isn’t.
Subreddit rules vary wildly (some ban promotional links outright, some require specific flair, some have cooldown periods between posts)
Getting flagged by a mod early destroys the warmup you need for this strategy to work.
Step 2: Find where AI is already citing
The goal isn’t popular subreddits.
It’s the specific communities where AI engines are already sourcing answers.
They set up campaigns targeting their core use cases:
reddgrow campaigns create \
--name "Reddit Marketing" \
--url "https://reddgrow.ai" \
--use-cases "find subreddits", "monitor brand mentions", "drive organic traffic" \
--talking-points "community-first", "no spam", "real conversations"Then added subreddits relevant to their audience — r/entrepreneur, r/startups, r/SaaS.
For me,
thinking about BoutPredict, the equivalent communities are
r/mmabetting, r/sportsbook, r/MMA, r/ufc.
The posts that would work aren’t “check out my fight prediction tool.”
They’re detailed fight breakdowns, honest takes on model accuracy, breakdowns of why a certain underdog won. The product comes up in context or not at all.
Step 3: Create content that AI will trust
They built what they called AI advocates means personas with different tones and voices that generate daily draft content for different communities:
reddgrow advocates create \
--name "Growth Hacker Persona" \
--role user \
--tone "analytical, direct, shares specific numbers" \
--campaign-id <id> \
--daily-drafts 3Claude reviews the queue every day, checks rules before recommending approval, and flags anything that looks promotional.
The early mistake:
they pushed volume too fast.
Posted too frequently, looked too product-adjacent, got an account flagged.
They slowed down to 2–3 posts per week per subreddit and everything stabilized.
The content that actually worked wasn’t about their product.
It was useful breakdowns of Reddit marketing strategy, community growth, AEO mechanics.
The product showed up naturally or not at all.
Before approving any draft with a URL:
reddgrow reddit subreddits check-url subredditname "https://reddgrow.ai"
reddgrow drafts approve <draft-id>Step 4: Distribute across every surface AI indexes
Reddit is the foundation, but the compounding happens when the same ideas appear on multiple platforms.
They used Postiz which is an open-source social media scheduler (31K GitHub stars) that supports 30+ platforms including LinkedIn, Medium, Dev.to, Hashnode, X, Threads, Bluesky, and more.
It has an MCP server, so Claude can drive scheduling directly.
The workflow: a Reddit post that performed well gets adapted for LinkedIn (slightly longer, more professional framing), condensed for X, expanded for Medium. Same core insight, different surface, staggered timing.
This is where the compounding actually starts.
Reddit posts to LinkedIn posts to Medium articles so each one is a separate citation signal pointing to the same ideas.
AI engines pick up the same concept from multiple authoritative sources and citation confidence goes up.
Step 5: Measure AI visibility directly
This was the part they hadn’t seen anyone else do systematically.
They tracked AI share of voice using ReddGrow’s built-in AEO tools.
Every day, the system runs target queries through ChatGPT, Perplexity, Gemini, and Google AI Overviews, then logs whether they were mentioned, the sentiment, and their position in the answer.
reddgrow aeo visibility timeline
reddgrow aeo brands ranking
reddgrow aeo citations list
reddgrow aeo sources top-domainsThe core metric is visibility_pct like the times mentioned divided by total scans, expressed as a percentage.
AI share of voice, essentially.
The curve looked like compound interest with a long flat start. Around 8 weeks of consistent posting before visibility moved at all.
Then it accelerated.
By week 10 they were at 40%.
Week 14: 61%.
That’s when the Stripe numbers started compounding.
The results
$17K MRR.
The traffic breakdown that mattered most:
Perplexity-referred users converted at nearly 6x the Google organic baseline.
The path wasn’t a hockey stick.
Eight weeks of almost nothing, then a sharp curve, then a plateau break.
The slow start is load-bearing like it’s the warmup period that builds the trust signals the AI engines need to start citing you consistently.
What didn’t work:
posting in too many subreddits at once, skipping warmup, and content that was too clearly product-adjacent.
These aren’t arbitrary rules.
The AI engines evaluate quality signals the same way human readers do.
If a post reads like marketing copy, it doesn’t get cited.
If it reads like a knowledgeable person sharing something useful, it does.
My take?
For BoutPredict, the immediate application is clear.
Detailed analysis on Substack.
Honest post-fight analysis on Reddit.
Model transparency posts like here’s what we predicted, here’s what happened, here’s why we think we were wrong.
That kind of content is exactly what Perplexity surfaces when someone asks “where can I find AI UFC predictions.”
The broader lesson for any micro-SaaS: you need to be on the surfaces AI engines trust before you need the traffic.
The lag is 8–10 weeks minimum.
If you start when you need it, you’re already too late.
Google isn’t dead.
But it’s no longer the only game.
The founders who figure out how to get cited by AI are building a distribution channel that the current SEO playbook doesn’t account for at all.
In case we are meeting for the first time, come over here, it’ll be worth the roller coaster of articles that are gonna come up in the next few weeks.
I swear tracking these updates is a job in itself, lately.
Here’s the list which I’ve built and keep adding on.
And If you need help for analyzing UFC fights, please check out BoutPredict :)