r/TechSEO

I'm building a web analysis platform focused on technical SEO, and I'd love to hear what other SEO professionals actually use in their daily workflow.

I'm not looking for a list of "nice-to-have" features. I'm more interested in the things that make you keep coming back to a tool instead of opening three different ones.

Some examples:

• Crawl analysis
• Core Web Vitals
• JavaScript rendering issues
• Internal linking analysis
• Structured data validation
• Indexability checks
• Log file analysis
• Internal PageRank / link equity visualization
• Duplicate content detection
• AI-powered recommendations

But I'm sure there are things I'm missing.

If you could design the perfect technical SEO tool today, what features would be non-negotiable?

Also curious about:

• what do existing tools (Screaming Frog, Ahrefs, Semrush, Sitebulb, JetOctopus, etc.) still do poorly?
• what wastes the most time during technical audits?
• is there anything AI could genuinely improve instead of just generating generic advice?

I'm looking for honest opinions from people doing technical SEO regularly.

Just wondering who are some of your favorites that provide valuable content and insight that’s not BS

I own an SEO agency in Brazil, and I have a fairly literal problem: my name is shared by at least two other public figures who show up in the same search results (a federal government official and a national newspaper reporter).

For years this meant a chunk of my "who is this person" signal was diluted or outright wrong whenever a model or a search feature tried to summarize me.

Over the past few months I tried a few things beyond standard Person schema.

FIRST STEP

First, an explicit llms-author.txt file separate from the main llms.txt, stating job title, agency, location and area of practice in plain sentences rather than relying on schema alone to carry that weight.

SECOND STEP

Second, adding the new Content-Signal header (ai-train=no, search=yes, ai-input=yes) to robots.txt, mostly out of curiosity about whether declaring intent at the header level changes anything measurable.

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Honestly, I don't have clean before and after numbers yet, this is closer to a live experiment than a case study.

What I can say is that Perplexity and Gemini answers referencing me as a SEO specialist have gotten more accurate over the last couple of months, though I can't fully separate that from the normal effect of more backlinks and mentions accumulating over time.

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What I'm actually curious about here: has anyone run a controlled test on Content-Signal headers specifically, isolated from other changes?

And for people who share a name with someone more famous or more indexed, what actually moved the needle, schema, sameAs links, a dedicated disambiguation page, something else entirely?

There are hundreds of platforms out there that can do keyword research and spit out a fresh SEO-optimized article in minutes. That part feels pretty solved at this point.

But here’s what I keep running into: I have an existing blog with posts from 2–3 years ago that are slowly dying (rankings slipping, internal links broken, etc) . The kind of “content decay” that apparently affects pretty much everyone.

What I actually need isn’t another tool to generate new content. I need something that can audit and refresh what I already have e.g update the stats, rework the structure for AI search visibility, fix the gaps vs. current SERP results.

Does that tool exist? Or is “historical optimization” still mostly a manual job?

Would love to hear what workflows people are actually using for this.

Hi

I’m in desperate need of some fresh eyes and technical advice regarding a sudden indexation collapse on my niche site.

Here is the background and the timeline:

The Site: It’s a highly focused, professional vertical site in the electrical/industrial niche (specifically targeting transfer switches: transferswitchhub.com). It's built on WordPress (WooCommerce with a customized Riode theme).

The Content: It’s NOT a scraped or spammy site. It's a hand-crafted project with a clean architecture, specific product categories based on technical specs (Amps, Poles, etc.), and product tables.

The Issue: The site used to have 6,000+ stable indexed pages and decent organic traffic. However, starting exactly on April 21, 2026, the indexation suffered a catastrophic cliff-dive.

Current Status in GSC:

Total indexed pages dropped down to basically 1 (only the homepage is left).

The remaining thousands of pages have all been shifted to "Crawled - currently not indexed".

Manual Actions: Clean. No red flags, no security issues, and absolutely no copyright/DMCA violations.

Tech Check: robots.txt is fine, noindex tags are NOT accidentally applied, and canonical tags are pointing to the correct self-referencing URLs. Googlebot has no technical issues crawling the site; it just refuses to index the pages after downloading them.

What I'm currently doing/suspecting:
Given the niche, the site naturally has many category and product variation pages based on specs (e.g., 100A, 200A, 3-Pole, 4-Pole). I suspect Google's recent core/spam algorithm rollouts might have flagged my template-heavy layouts or spec sheets as "low value" or "scaled content" by mistake.

To combat this, I am currently working on moving category descriptions from the top to the bottom and expanding them into deep, unique buyer guides/FAQs (300-500 words each) to increase the information density and break the template pattern.

My questions for the experts here:

Has anyone else in the B2C / industrial MRO niche experienced a massive "Crawled - currently not indexed" wipeout around mid-to-late April 2026?

Since Googlebot is actively crawling but withholding indexation, does this look more like a site-wide quality/authority suppression (algorithmic sandbox) or a pattern-matching issue?

What else should I investigate right now to signal "unique value" to Google and recover from this?

Any insights, brutal honesty, or technical checklists would be greatly appreciated. Thank you in advance!

Hey everyone, hoping to get some outside perspective on this.

Site: a taxi/transfer service site in Sri Lanka

For the past 3 months my Search Console performance was pretty stable averaging around 25-40 clicks/day and 800-1600 impressions/day, with one clear spike around late May/early June (hit \~60 clicks and 2.4K impressions in a single day).

Average CTR sits at 1.4%, average position 9.3.
Then in the last few days of the graph (right around 6/28-7/1), both clicks and impressions crashed to almost zero and have stayed flat since.

Total for the 3-month period: 1.85K clicks, 128K impressions.

A few things I’m trying to rule out:

No recent core update that I’m aware of in that exact window

Haven’t touched robots.txt or made major site changes recently

Not sure yet if it’s indexing-related, a manual action, or a technical issue (server/hosting/DNS)

Questions for anyone who’s seen this pattern before:
Is a cliff-drop like this (not a gradual decline) usually more indicative of a technical/crawl issue vs. an algorithmic hit?

What’s the first place you’d check Coverage report, manual actions, robots.txt, or something else?

Anyone else notice a wider pattern of drops around this same late-June/early-July timeframe?

Happy to share more screenshots if it helps diagnose.

Thanks in advance.

Quick question for the SEO folks here. I run a Shopify store and just built a dedicated brand page for one of our main manufacturers. The page has an SEO text that mentions their most popular product lines (which have their own product pages) and links to my generic category pages like accessories etc.

Now I'm second guessing myself. The brand page title targets "brand + product type" keywords, my category pages target the generic product type keywords, and the product pages target the specific model names. The brand text mentions those model names and links to them.

Is mentioning the models and categories on the brand page keyword cannibalization? Or is that only an issue if two pages target the same query in title/H1? My understanding is that internal links with descriptive anchors should actually help Google understand which page owns which keyword, but I want to make sure I'm not setting up my pages to compete against each other.

How do you guys structure brand page vs category page vs product page targeting?

Working on a local service business site with lots of pages and county location pages. Most of these pages are relatively thin service description, area-specific content, NAP and FAQ schema. Each page has LocalBusiness and FAQ structured data implemented via RankMath.

The question I keep coming back to: does adding structured data to thin pages actually help Googlebot prioritize crawling them, or does it add render weight that slows crawl efficiency on pages that are already borderline in terms of content depth?

Expected end result of technical SEO is more clicks, traffic and conversions. But the methods and strategies have always been subjective based on some insights/documentation from big G, speculation, trial and error rather than solid, decisive and objective.

Even with these "maybe works" strategies without obvious proofs or links to proven results, the search landscape is undergoing a tectonic shift - AEO / GEO. This changes the bottom line - traffic/conversion quite a bit.

It's clear that no one really knows what works or what could actually work (or not) tomorrow with solid proof.

How should technical SEO strategies and methods change or adapt?
Or perhaps start fresh with a completely new school of thought?

I was tinkering with mine before I quickly burned through credits. It’s flaky, but with some prompts a little scary how much it can tell you in a few minutes.

I’m noticing something on my site and curious if others have seen the same.

As the site gets larger, Googlebot seems to crawl new or updated pages more slowly than before. Nothing is blocked or noindexed, but crawl activity feels less responsive.

I’m wondering if this is just normal as a site scales, or if it usually points to something else: weaker internal linking, too many similar pages, lower content quality, or crawl budget getting spread too thin.

For larger content sites, what do you usually check first when crawl frequency starts slowing down?

That mindset shift has changed the way I think about SEO.

For years, most conversations centered around keywords, backlinks, rankings and technical fixes. Those things still matter, but they don't seem to tell the whole story anymore.

When reviewing a website now I find myself asking different questions:

• what is this business actually known for?
• why would someone trust it?
• what expertise does it demonstrate consistently?

Search engines appear to be getting better at understanding businesses not just pages. That makes building trust, credibility and a clear identity feel more important than ever.

Technical SEO and backlinks still matter, but they seem to work best when they're supporting a business that search engines can confidently understand.

has your approach to SEO changed over the last couple of years or do you still prioritize the same factors as before?

On larger sites, it feels like crawl efficiency is becoming more important than just getting pages indexed.

Reducing wasted crawl paths and improving structure has had more impact than simply adding more content.

Curious if others are seeing the same shift.

I swear some of these founders actively want to destroy their own domains.

My brain is already completely fried from finishing my baccalaureate exams this week, so when my biggest client slacks me panicking about a sudden traffic drop and saying "google penalized us" I almost quit on the spot

Spent four hours doing a technical deep dive. Checked search console, crawled the whole site for rendering issues, looked at server logs, checked for rogue canonicals. Everything was perfectly fine on the tech side

Turns out there was no seo penalty at all. their organic traffic was stable. what actually happened is they decided to do a massive "outreach" blast from their primary domain using some scraped list they bought. They got the domain completely blacklisted by major providers so none of their transactional or referral emails were landing, which looked like a massive traffic drop in their weird custom analytics dashboard.

I had to pull the raw csv they used and run it through mail tester ninja just to prove to the CEO that over half his "leads" were literal spam traps and dead addresses before he would believe me

now I have to explain to a grown man why a technical seo audit cant fix his broken domain reputation. Im just too exhausted for this tbh, kinda want to just go to my driving lesson and leave my phone on dnd for the rest of the day.

TL;DR: SEO is still important, but a big myth right now is that good Google rankings = good AI visibility. From my experience that is not true.

I've been building startups for years and until recently, I assumed if you ranked well on Google, the top LLMs would naturally pick you up too.

By now, it's fairly well known that AI pulls from way more than just search rankings. E.g. Reddit, review sites, docs, GitHub, comparison pages etc.

When building my latest tool I realised this is actually hilariously prevalent. ChatGPT, Claude, Gemini and co all pull from sometimes the most random locations. I am collating a comprehensive list of places, registries and domains which are commonly cited, but it really is surprising how little Google ranking seems to correlate with AI visibility. I say "seems" deliberately there as there is still decent correlation, just much weaker than you would expect. Now AI visibility optimisation is more important than ever

The future is no longer just SEO, but SEO + AI visibility

Thoughts?

I'm an experienced software engineer working in the space and have used most of the leading AI visibility software, every single one is misleading.

Basically, any tool claiming precision: "17% mention share", "rank 3", is false.

Not going to go into the specifics, but high level some key problems:
- Frontend scraping is unreliable
- LLM nondeterminism
- API vs. real app gap
- Limited prompt sets + scoring tricks
- Geography is usually ignored
- Model drift
- Personalization

That doesn't mean that the tools are completely useless they can show directional signals (e.g., “you’re invisible on commercial prompts” or “big gap in this geography”). But they shouldn’t claim precise, stable numbers without showing the underlying distribution, methodology, variance, and raw evidence.