Built a tool that finds which Reddit/HN threads are making ChatGPT recommend your competitors

This is the right shape of the problem but the hard middle layer is attribution lag. We've tracked AI citations for our marketplace product for 3 months — the Reddit/HN threads ChatGPT cites today were mostly planted 6-18 months ago. So seeding has a lag function: today's effort surfaces in answers next quarter, not next week.

Two things I'd add to the scoring: (1) thread age decay metric — older threads carry more weight in stable niches, less in fast-moving ones, depending on how often the model retrains for that topic. (2) competitor seeding intent inference — a meaningful fraction of 'organic-looking' founder comments in popular threads are placed marketing. Spotting them helps you understand what tone actually converts in the AI cite layer.

Curious what your confidence score weighs — thread upvotes? Recency? Outbound link count?

this is a real gap btw, spent the last week watching ChatGPT cite year-old reddit threads that have completely shifted what 'best X' means in some niches. AI citation tracking is going to be its own category soon.

one thing worth flagging though: the seeding kit angle lands very differently with different audiences. people in this thread will read it as competitive intel, but the same flow described as 'word-for-word comment copy you can drop in to shift the AI narrative' reads as astroturf to a non-founder. that's a brand-risk you might be inheriting from the framing more than the product.

could be worth two clearly separated modes: a passive one that just tells you which threads carry weight (great for SEO/positioning research, no concerns), and an active one with a stronger framing like 'genuine founder responses to threads where your product is already mentioned'. same engine, way less landmine.

curious if you've seen that split in how people respond to the demo.

The visibility into which threads influence AI answers is interesting. The part I’d question is how stable those signals are. Community threads can shift quickly, and AI outputs don’t always update in a predictable way.

Also feels like there’s a fine line between contributing to those discussions and trying to “seed” them. If it comes across the wrong way, it could backfire. Have you seen consistent movement in recommendations after engaging with the threads you surface?

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Smart positioning. One axis I'd love to see surfaced: not all citations are equal-weight in actual purchase decisions.

LLMs heavily prioritize threads where the recommendation answers a specific buying-stage question ('looking for X to do Y under €Z') vs. generic 'best Y tool 2026' lists. The former show up in real purchase-intent prompts, the latter mostly inflate visibility scores without converting.

Does AIRankCite differentiate between these in the visibility lift metric, or are all mentions weighted the same?

This is on my radar but I think the playbook breaks differently for non-English markets and it's worth flagging.

We're building an email/CRM/voice product for European SMBs. When I asked ChatGPT and Perplexity in French "meilleur logiciel email IA pour PME francaise" the recommendations are wildly thin - mostly translated US tools with bad French support. Almost nothing surfacing from French startup forums because those communities are smaller and AI engines weight English Reddit threads disproportionately.

My current hypothesis: for non-English builders, the citation game has two halves. Half 1 = English threads (Reddit, HN, IH) where you compete with everyone. Half 2 = local-language forums where you can dominate fast because incumbents ignore them. The local half pays off in 6 months when AI engines catch up to non-English content (already happening with Mistral and Claude in French, faster than expected).

Question for you: does AIRankCite handle non-English citation sources well? Specifically curious if you crawl French and German tech forums and weight them appropriately, or if you're English-Reddit-first like most tools in this space. That's the gap I'd pay for.

Going to run a scan on our domain.

This is one of those products where the wedge is stronger than the current name.
The real value is not “AI citation tracking.”
It’s competitive narrative control.
Founders are not paying to see which Reddit thread matters.
They’re paying to understand why competitors keep getting recommended and how to change that before it compounds into distribution loss.
That’s the real product.
AIRankCite explains what it does.
It also traps it in a narrow SEO/tooling frame.
If this expands beyond “find citations” into “shape recommendation share,” the product likely outgrows the current name fast.
Exirra.com fits that direction much better.
Sharper, more defensible, and broad enough to hold a larger competitive intelligence layer once this becomes less about tracking citations and more about controlling category narrative.