Hey everyone,

I'm building a service business that runs like software, not like an agency. Clients hand off recurring back-office work programmatic SEO, lead list building, CRM hygiene, follow-ups and I run an automated engine that does it, on their own tools and accounts. They just get the finished output.

The claim I'm trying to stress-test: adding a client should mean more supervision, not more headcount. My time goes into building and fixing the machine, not doing the work by hand. If that's not true in practice, the whole model collapses into an agency with better marketing, which is most of what's out there right now calling itself "service-as-software."

Before I scale, I want operators here to try to break this:

1. Where does this actually fail in practice? The invisible failure mode nobody talks about API changes breaking pipelines, clients fumbling their own accounts, edge-case QA quietly eating more time than doing it manually would have?
2. What would actually prove to you it's an engine and not relabeled manual labor? If I onboard a second and third client and my time-per-client doesn't drop, that's the tell it's not real. What would you look for as proof, if you were the buyer?
3. If you tried building this out yourself Clay, Persana, Unify, programmatic SEO scripts and went back to a VA or agency, what broke it? Where did automation stop being worth the maintenance?

Trying to find the crack before a client does. Fire away.

If you guys give interest in the comments ill share it here.

I run **PipeRocket Digital** so I will share what we see working across early stage and growth stage SaaS clients right now.

**What most SaaS teams get wrong:**

They start with blog content, chase high volume keywords, and wonder why organic never converts. Traffic without pipeline attribution is a vanity metric.

**What actually works in 2026:**

1. Fix technical SEO before any content. Crawl errors and duplicate pages kill authority quietly.
2. Build BOFU content first. Comparison pages, alternative pages, use case pages. These are the pages that convert buyers already in evaluation mode.
3. Map keywords to ICP, not just intent. B2B SaaS has multiple stakeholders. Each persona needs its own keyword set and content path.
4. Run programmatic SEO if you have integrations or verticals. This is how you scale organic without scaling headcount.
5. Optimise for AI search alongside Google. Structure FAQ content under 300 characters per answer. Use schema. Track your brand mentions in ChatGPT and Perplexity monthly.
6. Build topic clusters with a hub and spoke internal linking structure. Every cluster page links back to its hub. This is how you build topical authority fast.
7. Measure demos and trials from organic, not just sessions.

The biggest shift in 2026 is that SEO and AEO need to run together. Buyers are using AI search tools before they ever open Google. If your content is not structured for both, you are losing pipeline to competitors who are.

Happy to answer specific questions about your stage or category.

I have selected 30 keyword for my website

Like real estate cpa Houston, real estate tax planning in Houston i am publishing blogs creating topic cluster doing internal linking but still I am not ranking i am still on between google 3-6 page or sometimes not even ranking.

I don't know what is happening does anyone have a solution how should i target keywords in my content?

btw will mention creators too, if a creator of SEO tool is an SEO guy with 10+ years experience, tool is more likely to be high quality (imo)

otto by search atlas

manick bhan built this. ex wall street, switched to SEO, ran a real agency. won best ai search software last year beating semrush and ahrefs

good for agencies. 72+ tools, writes changes live, works on 14 CMS platforms, tracks GEO citations too. the most complete stack ive seen

downside is its a subscription and u still set the strategy, otto just executes it. so if you know nothing about SEO that's gonna be hard for you

surfer SEO

michal suski and slawek czajkowski built this from a small agency in 2017, no outside funding

good content editor, big community, proven rankings track record. but its not really agentic. it tells u what to do, u still do it yourself. it was acquired btw last year, so not sure if it's gonna keep that quality

roborank

flavio amiel built this solo. connects to wordpress, webflow or github and google search console, finds whats broken and writes the fixes directly to ur site. changelog with before and after for every change

looks like it was built by someone who doesn't like paying SEO tools that do nothing automatically. lifetime deal so no monthly fees, u bring ur own AI key

only downside is has a smaller feature set than the big platforms

frase

tomas ratia and cody jacques built this. focused on content. checks ur content for google rankings and AI citations at the same time

good if u post a lot. not great for fixing technical issues or bulk rewrites across existing pages

alli AI

kyle duck built this as an internal tool when he was consulting for staples, expedia and viacom

works on any CMS via a JS snippet, so no need to change platforms. good if u manage 20 plus client sites

insane thing is all ur changes revert when u cancel lol. also pricey and no community so if something breaks no one cares

so what we have is

otto or alli AI for agencies with lots of clients

surfer or frase for content teams

roborank for solo founders and people who hate monthly fees

AI search moves fast, but cutting through the noise is what we do. Here are the big topics the community rallied around this week:

• Cloudflare draws a hard line: AI has until September 15 to pay for content, or get blocked

Cloudflare will block AI training and agent crawlers on any ad-supported page by default from September 15, unless the site owner opts in. It's also upgrading last year's Pay Per Crawl tollbooth into Pay Per Use, compensating publishers when their content actually shapes an AI answer rather than merely when it's fetched.

CEO Matthew Prince's rationale: bots now drive more than half of all web traffic, so a sustainable ecosystem needs AI to pay for what it reads. Cloudflare even gave the new game a name — Answer Engine Optimization.

Source:

The Next Web

NBC News

____________________

• We finally have causal proof that AI Overviews gut publisher clicks — by roughly 40%.

A randomized field experiment with 1,065 Chrome users (researchers at ISB and Carnegie Mellon) found that when an AI Overview appears, outbound organic clicks fall 39.8% and zero-click searches climb to 34.5% — with no measurable gain in click quality or user satisfaction. 

Source: 

PPC Land

____________________

• Semrush's 126M-prompt study: getting mentioned by AI isn't the same as getting cited.

Semrush's expanded 2026 AI Visibility Index shows the platforms act like different channels: ChatGPT cites ~15 sources per answer and leans on Reddit and Wikipedia, while Gemini cites ~3. 

Only 36 of 1,200+ tracked brands stayed visible everywhere, every month — and 45% of marketing leaders still can't measure their AI visibility at all.

Source:

Semrush

PPC Land

____________________

• Google keeps re-tuning how publishers surface in AI — giving with one hand, taking with the other.

On the giving side, Google added a prominent visual treatment for recipe pages in AI Mode and is rolling a Top Stories news carousel into AI Overviews. 

On the other side, Discover now bundles multiple publishers under a single AI-written summary, with clicks often landing on just one outlet — another squeeze on referral traffic.

Source:

Barry Schwartz | Search Engine Roundtable

Lily Ray | Press Gazette

____________________

• A German court ruled Google owns its AI Overviews — mistakes included

The Munich Regional Court preliminarily held Google directly liable for false statements its AI Overviews generated, treating the synthesized output as Google's own content rather than third-party material. 

It's an early legal precedent that weakens the "the AI just wrote it" defense — and adds regulatory weight to the same publisher-vs-AI tension Cloudflare is now trying to price.

Source: 

Search Engine Land

And other AI LLMS text file questions answered for deep insight.

TL;DR

Yes, llms.txt does work — but only when you build all four LLMS files (llms.txt, llms‑ctx.txt, bing‑llms.txt, google-ai-overviews.txt) and follow a deliberate process. Most advice online is incomplete or flat‑out wrong. I spent days refining each file with the top AI models and documented what actually matters: company‑first structure, authority signals, consistent sections, and letting each AI model grade and improve its own file. This post answers the most common LLMS questions and gives you a practical section list you can use to build a best‑in‑class llms.txt for your own site.

Why I wrote this

Most LLMS.txt advice online is either incomplete or flat‑out wrong. After building all four LLMS files for a large, complex site, I wanted to share what actually works so others don’t have to spend days figuring it out.

Hi, fellow Redditors. I spent many hours/days working with the top 4 AI models to create near‑perfect LLMS text files for them. So I thought I’d share insights and answers to the most common questions, so others won’t have to work as hard. First, a disclaimer for SEO professionals thinking, “Hours? LLMS.txt is easy — shouldn’t take that long.” You’re correct if you have a small website with a couple dozen pages and one or two products. But when you’re dealing with a large site with hundreds of pages, diverse offerings, a unique business model, and a 30‑year company history… let’s just say you learn a lot about how to make LLMS files actually work. 🤪 Let’s start with the main question.

Does the llm.txt file really work?

Yes — especially for companies with large or complex websites. For smaller sites, it’s more about preparing for future needs. But regardless of size, LLMS files only work if done properly, and the process I used helps ensure they actually impact AI visibility. And when I say “yes, it works,” I’m referring to implementing all four LLMS files, not just llms.txt. Context is everything. You may have seen headlines like “Analysis of 300,000 domains shows llms.txt doesn’t impact how AI systems see or cite your content.” Catchy title — but dig deeper. Only ~30K sites even had llms.txt. No breakdown of:

• site size
• industry
• complexity
• number of offerings
• whether the file was implemented correctly
• whether the other three LLMS files were used

All of those matter. That article could be summarized in one sentence: About 10% of sites implemented llms.txt, and the other three LLMS files weren’t mentioned at all. Below are answers to the most common questions to help you understand the reasoning behind my approach.

What is an llms.txt file?

Contrary to what many SEO professionals say, llms.txt is not “a curated guide to your website.” It’s a curated Markdown guide to your company, designed to help AI agents understand your trusted content.

Is llms.txt a real thing?

Yes — it’s a new standard in the making, similar to how robots.txt started. But llms.txt is only ¼ of the system. You should create all four files:

llms.txt

bing-llms.txt

llms-ctx.txt

google-ai-overviews.txt

And no — llms-full.txt is not worth the effort. AI token limits mean they often only digest the first ~40 lines of llms.txt anyway. Sitemaps and robots.txt already cover full URL lists. llms-full.txt is something to revisit next year.

Does adding an llms.txt file improve SEO or AI visibility?

Not directly for traditional Google rankings. But it does increase the likelihood that AI models will accurately cite your company and understand your offerings. For large sites or sites with weak SEO in certain areas, LLMS files can help AI discover content it previously missed. There’s also a crossover effect between AI models.

Do major AI systems or Google use llms.txt?

Yes — especially when you implement all four files and keep them consistent and best‑in‑class. Here are the scores our files received (1–10), rated by each AI model:

• OpenAI LLMS: 9.8 (llms.txt)
• Bing/Perplexity LLMS: 9.9 (bing-llms.txt)
• Claude LLMS‑CTX: 9.5 (llms-ctx.txt)
• Google AI Overviews: 10 (google-ai-overviews.txt)

Google’s file is tiny and easy, but surprisingly, many don’t reach a score of 10.

What is the biggest and most common failure when creating llms.txt files?

Designing it with a website‑first approach instead of a company‑first approach. I started with “What do I want AI to know about our website?” After many iterations — and after each AI model critiqued its own file — the structure shifted toward a company‑first approach. Sections like:

• company metadata
• structured data
• primary audiences
• competitive differentiators
• FAQ
• published work
• industry recognition
• intellectual property
• owned brands
• entity aliases

…all became essential.

Does llms.txt replace robots.txt or sitemap.xml?

No. LLMS files are for AI ingestion. Robots.txt and sitemaps are for search engines and scrapers. Different tools, different purposes.

How to make an llms.txt file

Create a new llms.txt file and describe your company and website using plain‑text Markdown. Upload it to your website root so AI crawlers can find it. Then add this rule to your robots.txt:

Allow AI crawlers to access LLMS files
User-agent: * 
Allow: /llms.txt 

See the section list below for guidance. If you want a link to a best‑in‑class example, ask in the comments. After your first draft, ask ChatGPT to score it from 1 to 10. Implement the suggestions, then ask again. Repeat until your score is 9 or higher. Note: Some websites (including ours) block ChatGPT’s user window from accessing the file directly. That does not mean the bot can’t access it — only public users. If that happens, just paste the file content into ChatGPT.

What should be in an llms.txt file?

An llms.txt file should give AI models a clean, factual snapshot of your company: who you are, what you offer, who it’s for, and why you’re authoritative. It’s not a marketing page — it’s a structured knowledge file. llms.txt is the master file. Build it first, polish it, then reuse its sections across llms‑ctx.txt, bing‑llms.txt, and google-ai-overviews.txt.

Example llms.txt section list (use what applies, skip what doesn’t)

1. Company name and aliases

• One‑sentence identity summary

2. Website root

• Main homepage URL

3. Company metadata

• Founding year, ownership, certifications, BBB rating, etc.

4. Structured data

• Delivery modes (online, offline, video, onsite)
• Types of offers (courses, software, services)

5. Last Updated

• Helps AI know the file is current

6. Primary audiences

• Who your products are designed for

7. Core products and services

• High‑level product categories

8. Search keywords

• Terms users associate with your brand or offerings

9. Competitive differentiators

• What makes your products different or better

10. Product relationships

• How your products connect or complement each other

11. “If user wants…” sections

• Helps AI map user intent to the right product

12. Categories (for large or diverse sites)

• Organized product or content groupings

13. Frequently Asked Questions

• Short factual answers to common queries

14. Authority pages

• About, reviews, certifications, trust signals

15. Published work and industry recognition

• Trade magazines, media features, long‑term credibility

16. Citations and academic references

• Papers, journals, research that reference your work

17. Intellectual property

• Trademarks, proprietary methodologies, patents

18. Product ecosystem (if applicable)

• Brands, sub‑brands, product families

19. Entity aliases

• Alternate names users or publications use

20. Preferred citation pages

• Pages AI should reference when summarizing your brand

21. Contact information

• Support, sales, general inquiries

Why these sections matter

• Authority pages → help AI trust your brand
• Product relationships → help AI recommend the right product
• Competitive differentiators → help AI explain what makes your offerings unique
• Entity aliases → prevent misclassification
• Preferred citation pages → reduce hallucinations
• Search keywords → help AI map user queries to your content

How to test llms.txt

Ask the AI platform for the file it is designed for. It’s no different than asking a customer for feedback — you’re explaining your company to each AI model in the way it understands best.

Are major AI crawlers actually reading llms.txt yet?

Yes — if you follow this guide. No — for everyone else. SEO professionals often answer from a technical standpoint rather than from intent. It’s true that AI models aren’t “seeking out” llms.txt the way robots.txt or sitemaps are. But that negative reply is based solely on llms.txt — not the other three files. Even without directly opening llms.txt, the process above gets your information into the AI’s knowledge base. And when all four LLMS files are consistent, AI models treat your content with higher credibility. The bottom line: you want AI to understand your business more fully and clearly. LLMS files help you do that.

Advanced FAQs (often asked by developers or security‑minded users)

These questions come from people with a web development or data privacy background. They’re not essential for building an effective llms.txt file, but they help clear up misunderstandings.

Can llms.txt be used to block AI scrapers or prevent training?

No. LLMS files are informational only. Use server‑level controls (like htaccess rules) to block specific bots.

Should llms.txt include only key pages or a full sitemap?

Only key pages. Think of it as a “best foot forward” file. Include the pages that define your brand, establish your authority, and outline your core offerings.

If you want me to share links to our best‑in‑class LLMS files or have a question, just ask in the comments.

Statistics is a funny thing. When something grows from 0 clicks to 3 clicks, it makes it a 300% growth rate, to be sure. :)

AI traffic is 0.3% of all traffic for websites, on average, after three years of sending traffic, but we are still excited about reporting that :)

To be sure, traffic is not going to be a major AI visibility metric for a foreseeable future, since AI Answers are not designed for clicking. But it is nice that it grew to 0.3%

https://preview.redd.it/tdniyle68gah1.jpg?width=2048&format=pjpg&auto=webp&s=9f1048cdf7a9dd75b41bdc0dd8a3b74299386c5c

Source: Seranking

Hey everyone,

I’ve been navigating the agency ecosystem in India for a while now, and if there’s one thing I’ve realized, it’s that 90% of SEO agencies sound exactly the same during a pitch. Everyone promises "Page 1 rankings," "organic growth," and "transparent reporting," but the actual delivery varies wildly depending on what you actually need.

With search changing so fast—especially with Google's recent March 2026 core updates and the shift toward Generative Engine Optimization (GEO) and AI visibility (ranking in Gemini, ChatGPT, Perplexity)—generic monthly backlink packages just don’t cut it anymore.

I wanted to put together a realistic, fluff-free breakdown of the top SEO agencies in India based on actual industry reputation, technical capabilities, and execution style.

1. RepIndia (Best for Large Enterprise SEO, GEO & AEO)

If you are an enterprise brand or a mid-market company looking for sophisticated, multi-channel search visibility, RepIndia is easily one of the strongest heavy-hitters in the country right now.

The Vibe: High-end, strategic, and deeply analytical. They don't just do basic on-page tweaks; they handle massive site migrations and complex architecture.

Standout Edge: They were among the earliest legacy agencies in India to successfully pivot toward GEO (Generative Engine Optimization) and Answer Engine Optimization (AEO). If your goal is to ensure your brand is actively cited by LLMs and AI search engines alongside traditional SERPs, their technical team is top-tier.

Best For: Large-scale e-commerce, complex corporate sites, and brands targeting cutting-edge AI search visibility.

2. PageTraffic (Best for Structured Mid-Market & Global Campaigns)

One of the oldest and most established names in the Indian SEO space (around since the early 2000s).

The Vibe: Process-driven, traditional, and systematic.

Standout Edge: Because they’ve been around for over two decades, their documentation, backlink acquisition pipelines, and reporting frameworks are incredibly mature. They are excellent at taking mid-sized businesses and scaling their organic footprints globally.

Best For: B2B companies and competitive niches looking for highly structured, predictable monthly execution.

3. Schbang (Best for Content-Driven & Creative SEO)

Schbang is a massive integrated marketing agency, but their SEO wing shouldn't be overlooked if your strategy relies heavily on content.

The Vibe: Creative, integrated, and brand-first.

Standout Edge: Traditional SEO agencies often struggle to write content that actually sounds human and aligns with a creative brand voice. Schbang bridges the gap well by combining technical SEO data with a solid, high-quality content production engine.

Best For: D2C brands, lifestyle products, and companies where UX/UI and creative storytelling matter just as much as search data.

4. Techmagnate (Best for Performance & High-Volume Lead Gen)

Another veteran agency with a massive team that focuses heavily on the direct correlation between traffic and business revenue.

The Vibe: ROI-obsessed and data-heavy.

Standout Edge: They excel at high-volume lead generation campaigns and local SEO management at scale. They also offer solid vernacular (regional language) SEO, which is a massive growth lever in the Indian market right now.

Best For: BFSI, healthcare, education, and hyper-local service aggregator models.

Hey all, I have been researching CTR manipulation(I don't plan to do it). I see both sides. I feel that people who are against CTR manipulation say that it is not a real trust signal, it's unethical, etc.

But isn't buying backlinks the same exact thing? Its manipulation from other sites. I feel that this is not looked down on at all.

Or take writing content on a clients site. Google recently said that it does not want that. i.e How will a marketing agency in NY understand anything about a law firm in Nevada?

It almost seems that apart from technical SEO, everything else is "bad" and "frowned upon by Google".

Can someone help me understand why CTR manipulation is looked at so poorly yet most of what SEO agencies do is technically not allowed?

dont know if my way of doing SEO is the best one, so wanna know what you guys do for that

tried out roborank this week, automatically connected to my website and did the changes, works well.

maybe some other tools, or you so it manually?

for both SEO and GEO

how do u use GSC data?

anything that writes to the site automatically? or u do that manually?

if you have 100+ or 1000+ pages, what do you do with keyword cannibalization?

or it's stupid to have that many pages?

anything for keyword research? that's not super expensive?

If you were mentoring someone who's completely new to SEO, which skill would take the longest to teach?

I'm not asking which one is the hardest to learn. I mean the one where beginners usually need the most time before everything finally clicks.

It could be:

• Technical SEO
• Understanding search intent
• Link building
• Content strategy
• Analytics & reporting
• Client communication
• Something else entirely

In your experience, what makes that particular skill so difficult to teach?

I'd love to hear examples from people who've trained junior SEOs, interns, teammates, or even taught themselves over time.

Our client currently has an AI Visibility score of ~30 (Source - Semrush), and we've been tasked with improving it over the next 6 months. For those who have done similar projects, what activities drove the biggest improvements?

Would you point an AI backlink exchange (Distribb etc.) at your real projects, set it on a throwaway domain and link from there or not touch it?

Where's the line for you?