It was fun for the first few weeks building landing pages in 30 seconds, but trying to maintain a complex repo where half the logic was 'vibed' into existence is becoming a massive headache.

I feel like we’re accidentally trading an hour of typing for five hours of architectural debugging later on. I’ve started going back to manual typing for my core research logic just so I actually know where the technical debt is hiding.

Is anyone actually successfully managing a large-scale project with these agents, or are we all just building 'disposable software' now ?

Adding schema didn’t boost citations on any platform

We tracked 1,885 web pages that added JSON-LD schema between August 2025 and March 2026, matched them against 4,000 control pages, and measured citation changes across Google AI Overviews, AI Mode, and ChatGPT.

Adding schema produced no major uplift in citations on any platform.

AI source	Effect on citations	Verdict
Google AIO	−4.6%	Small but statistically significant decline relative to matched controls; (both groups were declining together, but treated pages fell slightly faster)
Google AI Mode	+2.4%	Statistically indistinguishable from zero
ChatGPT	+2.2%	Statistically indistinguishable from zero

These percentages come from our most reliable analysis (a matched difference-in-differences [DiD] test).

In this test, both AI Mode and ChatGPT treated pages performed slightly better than control pages on average, but the differences are small enough that they could easily be random noise across thousands of URLs.

AI Overviews showed a 4.6% decline, which is small but statistically significant relative to matched control pages.

But that isn’t quite the full story—we’ll get into that in the next section.

So, overall, we can’t tell whether the schema did a tiny bit of good or nothing at all.

Been noticing that some content consistently shows up in AI overviews while other content doesn’t get picked up at all

trying to understand what really makes the difference here

from what i’ve seen so far, it doesn’t feel like traditional SEO alone explains it

patterns i’m starting to notice:
• content that answers clearly and directly (almost like it’s written for extraction)
• structured sections (definitions, steps, summaries)
• strong topical consistency across the site
• content being referenced or echoed across different sources

it feels less like “ranking a page” and more like:
making your content easy to understand, reuse, and trust

also noticing that some pages with lower rankings still show up in AI summaries, which is interesting

curious how others are approaching this

are you intentionally structuring content for AI overviews now,
or just focusing on traditional SEO and letting it happen naturally?

Not generic one like clear content wins, content freshness matters, community forums make a difference etc. I really want to know the patters YOU have observed here.

Hi all! I wanted to share my workflow because I see the recommend me an API for SEO software question pop up here every other week and the answers are usually just brand names with no context.

Quick background: I run SEO for a small agency (8 clients, mostly B2B SaaS) and about 18 months ago I got tired of logging into 4 different dashboards every morning, so I started piping everything into a single Notion + Looker Studio setup through APIs.

• What I'm actually pulling:
• keyword positions daily
• backlink deltas weekly
• competitor rank changes
• GSC data
• on-page audit issues for our priority pages

Tbh the biggest unlock wasn't the data itself - it was building automated alerts so I find out about a lost #1 ranking before the client does.

For options on the market right now: Ahrefs and Semrush are the names everyone defaults to, but unless you're at an enterprise agency with a dedicated data team, the pricing and minimum commitments are honestly overkill - you end up paying for capacity you'll never touch.

Now I use SE Ranking - they expose rank tracking, backlinks, keyword research, and site audit data all through a single API, the pricing is per-request so costs scale with what you actually use, the data refresh cadence has been solid for client reporting, And lol, their support has answered every weird integration question I've thrown at them within a day.

Majestic is still around if you specifically care about link metrics.

If you're an SEO professional evaluating which one fits your stack, the questions that actually matter are: how often the index refreshes, whether all the modules (keywords, backlinks, audit) are accessible through one integration or you need three separate ones, and whether the rate limits hold up at scale.

Happy to answer specific questions if anyone's mid-evaluation - what are you all using and what made you commit?

Most knowledge management tools are still just fancy storage. You dump your policies and procedures in, agents search for what they need, and hope they find the right answer before the customer gets frustrated. It's a retrieval problem dressed up as a solution. What's been interesting about Panviva and what I didn't fully appreciate until we were a few months in — is that it operates more like a loop than a library. When a new compliance change comes in, it doesn't just sit in a document waiting to be found. Panviva surfaces it to the right agents, in context, at the moment they need it — mid-call, mid-workflow, based on what they're actually doing. The knowledge isn't passive. It moves. Their Sidekick assistant takes it further. Agents ask questions in plain English and get answers pulled from your organization's own approved knowledge base — not a generic LLM response, not a hallucinated answer from the open web. In regulated industries like healthcare and financial services, that distinction is everything. The AI is grounded in human-approved, compliance-vetted content. It's agentic in the sense that it's working in the background, routing the right information to the right person, without the agent having to go find it. Before this, we were stitching together a shared drive, a wiki, a training manual, and a Teams channel where managers answered the same questions on repeat. Five tools, zero coherence, and new agents taking four weeks to go live because there was no single source of truthNow training runs under two weeks. Compliance errors dropped. It's not fully autonomous. You still need humans maintaining the knowledge base and approving content before it gets served to agents. If your inputs are messy, the system scales that mess. But that's true of any agentic tool. The difference is that Panviva keeps running after setup. It's not a one-time output. It tracks what agents are searching for, surfaces gaps, and gives you the feedback loop to keep the knowledge current.

That compounding effect is what separates it from tools that just give you a report and call it done.Anyone else building out knowledge infrastructure with an agentic approach? Curious what the contact center side of this space looks like for others.