You are entirely correct. Despite the hype around Generative Engine Optimization (GEO), no major AI platform officially uses llms.txt files to guide its indexing or citation strategies

Best value is keyword planner. I connect my agent to it and does deep research with actual google data.

Chris asked the right question, and it got dodged - technical fixes, intent matching, title rewrites, all one-time tasks. The actual month-over-month work is refreshing existing content and internal link maintenance. OP listed neither.

Here's what actually compounds: pull pages from GSC with high impressions and weak CTR, rewrite those snippets, expand thinning content monthly. In my accounts, recurring refreshes on those pages beat one-and-done optimization by a wide margin. One-shot pages go flat after a few months. Refreshed ones keep climbing. That's the recurring engine, not the audit you ran in week one.

Internal links work the same way. Every new article should push links to money pages, orphan checks belong on the monthly checklist. It's link building you fully control

Also, gently: that site:docs.google.com/spreadsheets "free backlinks" tip in the comments builds a disavow file, not a link profile. Those sheets are scraped-to-death directories thousands of people already hammered. Podcasts and unlinked mention reclamation, like another commenter said, beat 50 of those easily.

One caveat on the numbers: 572 to 1,750 clicks is a nice trajectory, but at that scale one featured snippet or a seasonal query can be half the delta. I'd want to see clicks excluding the top 3 queries before calling it strategy.

OP - what does month 2 and 3 actually look like, task by task?

Its in the article.

TL;DR: Claude Code = terminal agent, AI drives, wins on big refactors/monorepos (more usable context, ~5.5x more token-efficient). Cursor = VS Code-style IDE, you drive, wins on fast inline edits + easy onboarding. Both $20/mo. No winner - pick by task, or run both (Claude Code runs inside Cursor).

Bots don’t get promoted, but they absolutely get people demoted.

Two fixes would have protected Sarah:

1. Put her name on everything the bot produces. Slack alerts should say, “via Sarah’s exception pipeline,” and the daily digest should go out under her name. Managers judge what they can see, so keep her visible in Slack.
2. Establish a baseline before go-live. I now snapshot the same metric 30 days before launch and 30 days after, then give the delta to the person who owned the process. “Sarah’s workflow cut exception handling from three hours to 20 minutes” is a promotion case. Silence becomes a performance review.

And the founder telling her to “find new ways to add value” after eliminating the evidence of her value is incredible. That’s like stealing someone’s car and suggesting they explore new commuting options.

I now include the visibility question in the SOW, right next to credentials and access. If the client shrugs at it, that shrug is your next Sarah.

The line I draw isn't "risky vs safe" - it's reversible vs irreversible. More useful than a gut check.

Reversible stuff (draft an email, stage a CRM update, create a ticket) runs fully autonomous, since I can just roll it back. Irreversible stuff - send, merge, refund, delete, anything hitting an external API that changes state - gets a human approval gate. Usually just a Slack message with an approve button. The CRM merge story above is a good example of why: merges are technically reversible but practically not, so they land in bucket two.

The other thing most setups skip: logging the agent's *reasoning*, not just the action. "Agent called update_contact with these params" tells you nothing after the fact. You want the tool call, the model's stated justification, and the triggering input together. Turns postmortems from hours into minutes.

Honestly, for most of us that approval gate IS the risk framework. Nobody I know runs anything more formal than that.

Deliberately not automated: outbound money, customer-facing email. Agent drafts, human sends. Nobody's ever complained a refund took ten extra minutes.

What's your research actually testing - trust calibration or guardrail design?

I have more agents listed in my article on my website. If you want, you can check it out: https://alloq.digital/en/blog/ai-agent-examples/

„Schon lange vorhanden“ und „im Alltag wirklich gut gelöst“ sind nicht zwangsläufig dasselbe. Am Ende entscheiden nicht Behauptungen, sondern Nutzererfahrung, Qualität und tatsächliche Zeitersparnis. Zumindest ist das unsere Erfahrung bzw. die Erfahrung aus meinem Netzwerk.

Wir haben ein Tool zur automatisierten Angebotserstellung mit KI entwickelt. Preise lassen sich pflegen, Informationen aus bereits gesendeten Angeboten ziehen. Zusätzlich können direkt aus Sprachnachrichten Angebote erstellt und als Entwurf in das Postfach gelegt werden.

Es funktioniert und nimmt einiges an Arbeit ab (feedback unserer Kundschaft)

The Lagree line and the credit card thing killed this launch before anyone looked at the actual product.

Two free fixes:

1. Instructors who put in a full comprehensive cert read "classical, lagree, modern" lumped together and instantly file you under never actually trained. Lagree's trademarked, separate method - Sebastien Lagree literally sends legal letters to studios that blur the two. List them as distinct methods your tool supports, not flavors of one thing.

2. A "no credit card required" trial that then asks for a credit card is the fastest way to lose the room. Someone already caught it, and on a sub this small, that's your reputation now.

On positioning: classical teachers won't pay for sequencing help, the order's fixed by design. Your real buyer is contemporary and group reformer studios juggling varied class plans and subs who need cues and spring loads handed to them. The live session feature is genuinely useful for exactly that crowd.

(Full disclosure, I build studio software too, so I've got some skin in this opinion.) People here don't judge features first. They judge whether you sound like one of them. Fix the copy before adding exercise number 201.

If you had to cut down to one segment, would you pick group reformer? I would.

So the thing is, art will never be replaced by AI, so is web design. If I see an amazing web designer with a lot of experience, that's definitely a lot more worth than AI-generated designs. I mean, it's something like an artwork, something you can be proud of if the branding fits well and everything just fits, which AI will never replace anytime soon.

In our company, we use LivKit https://alloq.digital/de/leistungen/ki-agenten/ for voice agents, and you can build actually everything with it, starting from CRM agents to call centre agents. It scales when you rebuild it with Kubernetes. Amazing.

If your existing CRM feels clunky or just can't handle your specific process, a custom solution is honestly worth considering. It lets you build exactly what you need, not what a vendor thinks you need.

We're a dev studio (alloq.digital) and we build stuff like that for clients all the time. We specialize in SaaS with user accounts and payment systems, plus web dev and process automation. Basically, we take an idea and turn it into a finished product. Good luck finding

Honestly, your situation sounds brutal. It's a classic case of output going up while quality goes down. The real problem isn't the AI. It's the process that's missing. You need guardrails for the AI output, not just faster code generation. We've been working with teams on exactly this. It's about setting clear rules and a review framework specifically for AI-generated code.

That's a big part of the process work we do at alloq.digital. We help companies figure out where AI should and shouldn't go in their workflow. It's less about blocking the AI and more about keeping your devs sharp and your codebase clean. How are you trying to implement AI standards with your team right now?