r/Discover_AI_Tools

Been thinking about what actually makes brands act on AI visibility, and it’s never the number. Nobody moves because they got a 58.

What moves people is seeing the actual answer: “when someone asks for the best [your category], the model recommends your competitor by name.” That’s not a metric, that’s a receipt.

Which makes me think the whole space is framing this wrong. We keep talking about scores and rank-style tracking, but the unit that matters is the query-level outcome: who got named, who got skipped, and what sources the model leaned on to decide.

Curious if others see the same thing — do clients/teams react to scores, or only when they see the raw answers?

Hello, I'm currently a high school student interested in computer science and engineering. I'm somewhat proficient in basic Python, I'm learning C++, and I've been experimenting with the Elegoo starter kit. I'm also trying vibe coding for the first time just for fun, so for my first project I used Lovable to build a website with a built in AI for research purposes.

The idea is that you type in your research question and the AI searches through different databases (I could only find free API keys for [ClinicalTrials.gov](http://ClinicalTrials.gov), OpenAlex, PubMed, Semantic Scholar, arXiv, Europe PMC, Crossref, DOAJ, OpenAIRE, and Zenodo), then rates how well researched the topic is, gives a timeline of the research, and lists any gaps in the existing research. There's also a social feature where you can create a group and work together with others.

I'm genuinely wondering if this is a useful feature, because I feel like any AI could already do what I'm building. If it's not that useful, are there other directions somewhat similar to my current website that I could pivot it toward? Or are there any other features I could add to my app to separate it from what currently exists?

A few years ago, most companies were focused on rankings, backlinks, and organic traffic. The goal was simple: get to the top of Google.

Now it feels like the conversation is changing. Instead of just ranking pages, companies want to be included in the answers themselves.

Some of the reasons I keep hearing are:

• AI tools are becoming a discovery channel, not just search engines.

• Being recommended by AI can create highly qualified, high-intent traffic.

• Brand mentions, authority, and entity signals seem to matter more than ever.

• Companies want visibility wherever customers are searching for information.

So if you're evaluating agencies today, what should you actually be looking for? Experience with GEO? AI visibility tracking? Entity optimization? Content strategies designed for LLMs?

I’m building Relevyn — basically “SEO for AI.”
It tracks how often AI assistants (ChatGPT, Claude, Gemini, etc.) mention your brand, gives you an AI visibility score, and shows what to improve so you get recommended more often. Check out the site and tell me what I can do to improve!

For local businesses, “near me” used to basically mean Google Maps and maybe a few review sites.

Now you’ve got people asking AI tools stuff like:

* “Which plumber near me actually shows up on time?”
* “Best med spa in \[city\] for Botox?”

A lot of those answers are being influenced by:

* How often you’re mentioned on Reddit and niche forums.
* Whether your info is consistent across the web.
* The content structure on your own site and profiles.

But here’s the issue: most owners have zero way to see if they’re even appearing in those AI answers, let alone whether the description is accurate or positive.

For those doing local SEO or running local businesses:

* Are you trying to measure “AI visibility” at all?
* Are you just doubling down on traditional local SEO and hoping AI follows?
* Have you seen any real-world impact (good or bad) from AI tools recommending or ignoring you?

Would love to hear how people are thinking about this, especially agencies doing local campaigns.

Most SEO advice for AI search stops at "make an llms.txt." After digging into how ChatGPT, Claude, Gemini, and Perplexity actually pick what to cite, I ended up deploying a fuller set of root files. Sharing the breakdown because the llms.txt-only advice is leaving a lot on the table.

The 7 categories I landed on:

• Identity & permissions — robots.txt (with per-bot rules for GPTBot/ClaudeBot/PerplexityBot) and ai.txt (who you are, what you're known for)
• Content — llms.txt (the cheat sheet) and llms-full.txt (the deep dive)
• Map & navigation — ai-sitemap.xml with plain-English summaries per page, plus a Markdown sitemap
• Intelligence — ai-entities.json, ai-intent.json (maps real user questions → your best page), ai-schema.json
• Research/RAG — rag-index.json + .jsonl so retrieval pipelines stay grounded in your content
• Policy — training-data-policy.txt, ai-disclosure.txt
• Operations — deployment checklist, manifest, structured-data guide

Two things that surprised me:

1. Serving a clean Markdown mirror of your homepage (/index.md) and pointing AI crawlers to it in robots.txt noticeably changes what gets ingested vs. the JS-heavy HTML.
2. The Princeton/Georgia Tech GEO study (KDD 2024) found citing sources and adding statistics each lift AI visibility ~37–40%. Structure alone isn't enough — the content has to be citation-shaped.

I open-sourced the generator (MIT) so you don't have to write these by hand — happy to share the link in a comment if that's allowed, or you can search "AI Readiness Kit." Curious what others here are doing beyond llms.txt — anyone tracking whether these files actually move citations?