About

I help B2B brands win the next era of search. The one happening inside ChatGPT, Gemini, Perplexity, and Google's AI Overviews. Right now, your buyers are asking AI engines full questions and getting back one answer. The answer mentions a few brands and ignores the rest. If you're not in that answer, you don't exist in their consideration set. That's why I'm building Zaraftis. Zaraftis is the analytics platform for AI search visibility. We track AI Visibility, Share of Voice, Citation Share, and Sentiment across every major AI engine, at the prompt level. We tell brands exactly which queries they're losing, to whom, and what to ship to close the gap. The 175-point AI-readability audit turns measurement into a prioritized action playbook your team can actually ship.

Badges

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Gone streaking 10
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Maker History

  • Zaraftis
    ZaraftisAI gives ONE answer. Be in it.
    Sep 2026
  • ReelCraft
    ReelCraftIdea to Animated Stories in Minutes
    Jan 2024
  • 🎉
    Joined Product HuntAugust 12th, 2023

Forums

6d ago

Zaraftis - AI gives ONE answer. Be in it.

AI search is rewriting how buyers discover brands. Zaraftis shows where you’re missing from AI answers, why competitors are being cited instead, and exactly what to fix next with ranked, actionable insights across AI platforms.

3mo ago

How Are You Measuring Your Brand’s Visibility in AI Answers?

Are you using a dedicated tool, checking prompts manually, or not measuring this yet?

I m especially curious about:

  • Which AI platforms do you monitor?

  • Do you track mentions, citations, sentiment, or share of voice?

  • How do you choose the prompts worth tracking?

  • Have you found a tool you genuinely trust?

  • What is still missing from the tools you have tried?

Would love to hear what your current workflow looks like.

“AI Slop” - When Optimization Metrics Replace Human Readability

There s a growing pattern of using tokens to generate AI code and documentation slop. Then use even more tokens to understand and review that slop.

Then judge engineers by token usage instead of how empathetic and clear their docs and code actually are

At some point, the system starts optimizing for the wrong thing. Instead of asking Can a human actually work with this? , we continue asking How much did we generate? or How many agents did we spin up today? - are those the success metrics we want?

A clear example of this is what we re seeing in AI-generated UIs for landing pages. Tools like Claude (and others) can produce interfaces quickly, but they often converge into a very recognizable template. Same layout patterns, same spacing, same visual language. It becomes less design and more average of all designs the model has seen.  

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