u/SERPArchitect

I recently worked with a client who used AI to revamp their product pages and saw a solid traffic jump pretty fast. The speed and scale are honestly impressive, what used to take days now takes hours.

But at the same time, I’m seeing a lot of thin, AI-heavy pages ranking over genuinely useful content with real insights. Feels like we’re trading depth for volume. Curious where others land on this, is AI helping SEO evolve, or slowly breaking it?

A lot of advice I see is still surface-level, tweaking intros, tightening copy, improving headings, etc. Useful, sure, but it feels like small edits, not real strategy.

I’m more interested in what’s changed behind the scenes: how you pick topics, prioritize pages, evaluate trust signals, or even decide what not to publish. Are you changing workflows, processes, or team decisions because of AI search, or just polishing content differently?

I’ve used Profound before, and it’s solid for tracking AI visibility, citations, prompts, etc. But honestly, it felt very insights only, you still have to figure out execution yourself.Switched to Quattr and the biggest difference is it actually closes that gap.

What stood out for me:

• It doesn’t just track AI visibility, it connects it to GSC + GA4, so we can see what’s actually driving traffic and conversions.
• Has GIGA AI that analyzes pages vs competitors and suggests + generates optimizations
• Automated internal linking at scale (this alone saved a lot of manual work)

So yeah, Profound is good if you just want monitoring. But if you actually want to act on that data and scale it, Quattr has been a much better alternative in my experience.

I’m not talking about generic advice like “write high-quality content.” I mean what are you specifically changing on your pages to improve the chances of getting picked up or cited?

Are you experimenting with things like:

• Putting direct answers right at the top
• Breaking content into smaller, self-contained sections
• Using FAQs or structured formats
• Adding schema or focusing on entities/topics more clearly
• Building credibility signals (data, sources, mentions)

I want to know what’s actually working for people vs what just sounds good in theory.

Most B2B SaaS companies think publishing more blogs or SEO pages will improve their visibility in AI. It won’t.

AI doesn’t “rank” like search engines, it interprets. And that’s where most brands fall short.

First, there’s no consistent identity. Your website, LinkedIn, and other channels say slightly different things, so AI can’t clearly understand what you do.

Second, teams track traffic and rankings, but not how AI systems actually describe or categorize them. If the model gets your positioning wrong, you won’t show up.

Third, brands assume their reputation carries over automatically. It doesn’t. Authority needs to be consistent, structured, and reinforced across multiple sources.

The real issue? Most companies aren’t checking how AI perceives them at all. No audits, no monitoring, no correction.

So when someone asks AI for recommendations, you’re invisible.

Fixing this isn’t about creating more content. It’s about aligning your narrative and controlling how AI interprets your brand.

For B2B especially, it feels harder to prove real expertise compared to consumer content. Are you focusing more on author credibility, case studies, first-party data, or brand mentions? And what has actually moved rankings or visibility for you lately?

I went pretty deep testing AI detectors this year and honestly most of them look good until you stress test them. I ran pure AI text, fully human writing, and then “humanized” AI content through multiple tools to see what actually holds up. That last one is where things got interesting because a lot of detectors just break once the text is slightly reworked.

One tool that stood out for me was Proofademic. It consistently picked up paraphrased AI content that others missed, and instead of just giving a score, it showed exactly which parts were flagged and why. That made it way easier to trust the results, especially since false positives on my own writing were very low compared to others.

Most of the popular tools still work fine for obvious AI text, but they struggle when content is edited or rewritten. If you just need a quick check, something like GPTZero is okay, but if accuracy and clarity actually matter, especially for academic use, you need something that can handle paraphrased content and explain its reasoning properly.

I’ve been focusing on things like blog content and on-page optimization, but growth feels slow and I’m not sure what to try beyond this. Backlinks also seem hard to build without actively promoting, which makes it tricky.

The idea itself is working based on user feedback, so I know there’s potential. Just looking for better ways to scale traffic and improve rankings from here.