The primary time I noticed my consumer’s model in an AI reply, no person had finished something to place it there.
I used to be working by one of many first workouts in an AirOps automation course, working a number of prompts by ChatGPT, Perplexity, and Google AI Overviews to see what got here again.
After I did, Perplexity talked about CAT Electrical Imaginative and prescient, a small agency that sells earthing and lightning surge safety gear on the Romanian market, proper in its reply. In ChatGPT, the identical model solely confirmed up as a buried hyperlink within the sources panel.
The agency has been promoting for 3 years, had a workforce with over a decade of expertise, and I do know it properly as a result of the consumer is a detailed relative. Like numerous small, native companies, they’d been doing wonderful on referrals, returning enterprise shoppers, and being discovered on-line. Content material advertising got here in bursts: an preliminary push to arrange model property and a content material technique, then enterprise would choose up, the web presence would decelerate, and getting again to it meant a reconnecting section every time.
Even so, the model was already showing in AI search with out anybody engaged on it. It acquired me pondering: if it confirmed up organically, then with extra effort behind it, possibly it might present up extra typically and throughout extra engines?
That is what AEO (reply engine optimization) comes right down to: engaged on how typically AI engines point out you when somebody asks a query you can reply. It is one thing most entrepreneurs haven’t got a system for but, and neither did I.
I am a generalist content material marketer, and this pipeline was my first automation construct. However I am curious; I search for possibilities to upskill, and because of my course, I knew how one can use AirOps, a content material optimization software. So I adopted the thread: if individuals are asking AI engines questions the model might reply, how do I discover these questions?
What I ended up with is a content material loop that runs about as soon as per week. It checks what the model has already printed and the way it carried out, finds the questions the place the model is invisible in AI solutions, checks whether or not the corporate has sufficient materials to credibly reply them, and turns the most effective ones into writer-ready LinkedIn briefs that land in Buffer.
Here is how I constructed it (and what I would take into accout if you wish to attempt one thing related).
Why LinkedIn was the plain place to begin
Across the time I used to be working these immediate workouts, I stored coming throughout analysis on the place AI engines pull their solutions from, and LinkedIn got here up repeatedly.
Semrush discovered that LinkedIn is the second-most-cited supply throughout ChatGPT Search, Google AI Mode, and Perplexity, showing in 11% of AI responses on common. For skilled queries particularly, Profound discovered LinkedIn is essentially the most cited area throughout all six main AI platforms, together with ChatGPT, Gemini, and Copilot.
That second stat mattered most for this consumer. The folks asking about surge safety are engineers, installers, and constructing homeowners — skilled queries by definition. There’s even information on what will get a LinkedIn put up cited: Scrunch discovered that technical particulars elevate the percentages by 77% and named entities by 33%, whereas Unicode daring formatting lowers them by 58% on ChatGPT particularly. A small agency with deep technical experience is properly positioned for precisely that form of put up.
The agency additionally had a head begin. They had been already utilizing LinkedIn to succeed in shoppers and had older materials to construct on. And LinkedIn posts take much less effort to put in writing than weblog articles, which is good for a small workforce whose content material advertising occurs in bursts.
One resolution I made early was to trace model mentions quite than citations. A quotation is when an AI engine hyperlinks to your put up as a supply; a point out is when it names your model within the reply itself. Peec AI, the visibility software I will get to shortly, tracks the 2 individually. For a agency whose shoppers ask questions like “suggest a lightning safety system provider in Romania,” being named within the reply is a big win.
📚 Why LinkedIn Is the Most-Cited Supply in AI Search (and What Your Enterprise Ought to Do Subsequent)
The stack I landed on
The completed loop runs on 4 instruments, every with one job:
- AirOps orchestrates the entire workflow and holds the corporate’s Model Equipment and Information Bases
- Peec AI tracks which AI prompts point out the model, and which do not
- Buffer provides the LinkedIn engagement information and is the editorial house the place completed briefs are despatched for the author to assessment
Right here’s why I selected every software.
AirOps, the orchestration layer
AirOps was my introduction to automation, by the identical schooling cohort the place I ran these first immediate workouts. It may be an costly software, however the trial interval covers an experimentation section like this one, and it comes with an AI agent referred to as Quill. I created the workflow by conversationally sharing my concepts with Quill and having it type out the technical elements, which made the entire thing a lot much less intimidating for a primary construct.
AirOps additionally helps you create a Model Equipment and Information Bases contained in the platform. As soon as the corporate’s product pages, social posts, and YouTube transcripts had been in a single place, I grew to become extra conscious of how a lot unused data the agency had, and I began exploring methods to show it into content material concepts.
Peec AI, for visibility information that covers the Romanian market
The deciding issue right here was language. AirOps has its personal Prompts function, nevertheless it did not assist Romanian but after I constructed the workflow. Scrunch focuses on English prompts too, and Profound and AthenaHQ, at first look, are constructed for enterprise groups quite than a small agency experimenting.
Peec AI confirmed up in my LinkedIn feed someday, and it turned out to work properly for the Romanian language and market I had in thoughts. They’re headquartered in Germany, which could clarify the local-market protection, and there is a trial interval as properly.
Buffer, for engagement information and because the editorial house
The workflow solely works as a loop if it could possibly see how the final spherical carried out. Buffer is a LinkedIn advertising associate, so engagement information for posts printed by it comes again by Buffer’s personal API, the identical one the workflow was already speaking to.
Buffer picked up the second job as soon as I noticed that put up concepts can land within the Create board by the API. I did not need the author tied to the workflow or needing AirOps entry, and Buffer solved that with out including one other layer like a Google Doc or a Notion board.
The Create board is structured like a kanban (a board of columns the place work strikes by phases), which matches how I am used to seeing editorial work organized. A number of writers can have entry, an editor or the proprietor can approve posts earlier than scheduling, and all of it stays separate from the automation itself.
How the workflow runs, finish to finish
The workflow runs as soon as per week. Every run opens by checking what already occurred — what acquired scheduled, what acquired posted, and the way it carried out — earlier than deciding what to make subsequent. From there, it really works by 4 steps.
Step 1: Test what’s already in movement
The AirOps Playbook agent connects to Buffer by the API to see what’s already drafted or queued, so it would not create duplicate concepts. It additionally checks its personal inner Grid, the workflow’s reminiscence of earlier runs. Then it pulls engagement information for printed LinkedIn posts by the identical Buffer API, reads the dataset that comes again, and extracts reactions, feedback, impressions, and attain for every put up. Posts that carried out properly develop into alerts for follow-up content material.
This step replaces an individual having to open the apps themselves or message workforce members to stand up up to now.
Step 2: Ask the place the model is invisible
Subsequent, the agent connects to Peec AI by a token and pulls information in two passes. The primary brings again the prompts themselves: textual content, subject, quantity, and tags. The second brings the measurements: visibility, share of voice, sentiment, place, and the way opponents present up for a similar prompts.
Peec runs the tracked prompts day by day throughout three AI engines (ChatGPT, Perplexity, and Google AI Overviews) and data whether or not the model is talked about in every reply, so a immediate’s visibility rating is the share of solutions that point out it. The workflow flags a immediate as low visibility when it sits at 0% after no less than seven days of monitoring, drops beneath 20%, or falls 15 factors or extra in a month.
🖊️ In observe, the thresholds do not filter a lot but, for the reason that model’s general visibility is 7% and practically each immediate certified at 0%. However this step will matter extra as visibility grows.
In a nutshell, this step solutions the query “The place are we invisible?” It replaces a marketer manually going by the prompts, doing information entry, and exporting CSVs.
Step 3: Map the gaps towards what the corporate can credibly say
For every immediate, the agent searches the three Information Bases: 390 older product pages, the corporate’s social media posts, and YouTube transcripts. It checks whether or not the corporate has sufficient materials to credibly reply every query; prompts with no supporting proof get dropped, since with out materials to attract on, the thought can be hole.
Surviving concepts get ranked on 4 elements: the dimensions of the visibility hole, how typically folks ask the query, the energy of the Information Base proof, and whether or not opponents already personal the reply area. Solely the highest 5 transfer ahead, a restrict I set on how a lot we will work on without delay.
Then the agent pulls the Model Equipment guidelines (the LinkedIn put up format, persona, viewers profiles, and writing guidelines) and combines them right into a single self-contained transient per immediate: subject, angle, viewers, key factors, model positioning, verbatim supply materials, and format steerage.
Step 4: Land in Buffer as writer-ready briefs
The agent sends every transient to Buffer with a single API name, and it exhibits up in Concepts, contained in the Create area. When the author opens Buffer, an thought appears to be like like this:
- Put up thought: the subject, angle, which viewers to put in writing for, and a proposed day to schedule. For the thought within the screenshot, the agent focused buyers and constructing beneficiaries, flagged it as excessive precedence, and proposed a Wednesday slot.
- AEO prompts focused: the 2 Peec prompts the put up is supposed to reply, each at 0% visibility with medium quantity.
- Key factors: the substance of the put up. This one suggests explaining that “set up included” is a advertising phrase, not a certification, and {that a} consumer ought to ask who designed the system and whether or not the venture complies with the technical commonplace.
- Model positioning: how one can place the corporate as the reply: the certification stack, 2,500+ accomplished tasks, and a producer whose merchandise are independently lab-tested.
- Merchandise to call: two particular lightning rods.
- Supply materials: excerpts from older LinkedIn posts pulled from the information base, so the author attracts on actual product language.
- Put up format: the Model Equipment guidelines are 50 to 299 phrases, no Unicode daring, hyperlink within the physique, direct tackle, open with a direct assertion. The no-Unicode rule comes straight from the sooner quotation information.
On the subsequent run, the agent checks whether or not the put up went stay, pulls its metrics from Buffer, and checks Peec once more to see whether or not the model is now talked about for that immediate. That examine is what closes the loop.
What’s modified up to now
It is too early for spectacular numbers. The posts created from the pipeline’s briefs are simply going stay, so there is no change in cadence or publish charge to report but.
What has modified is that the place my concepts used to stall in a brainstorming section, they now arrive in Buffer as structured briefs, and the author spends much less time reviewing. We’re not posting 4 instances per week but, however we now have the capability to.
On visibility, 4 of the 5 prompts the pipeline chosen are now not at 0% in Peec, and the posts weren’t stay but, so I can not attribute the change to the pipeline. Not each visibility shift might be traced to new content material, and it is helpful to know that earlier than you promise a consumer in any other case.
The clearest end result up to now is buy-in. Earlier than this, AI search wasn’t on the proprietor’s radar; their focus was compliance, EU guidelines, and e mail campaigns. The experiment put AI search on the record, and that is a lead to itself.
What I am awaiting subsequent is whether or not the printed posts transfer their prompts off 0%, and whether or not concepts from the pipeline make it to publish at the next charge than concepts did earlier than.
If you wish to attempt one thing related
You do not want this actual stack. The lighter-weight model appears to be like like this:
- Run the invention train your self. Ask ChatGPT, Perplexity, and Google AI Overviews the questions your shoppers ask you, and notice the place the model exhibits up and the place it would not. The 2 inquiries to give attention to: how is the model talked about in AI searches, and the place are the engines failing to say it when somebody asks one thing related?
- Centralize what the corporate can credibly say. I used AirOps’ Model Equipment and Information Bases, however a set of Notion pages or Google Drive docs works too: an organization description, a product and repair record, experience areas, and your LinkedIn put up format guidelines. With a Notion or Drive MCP connector, an LLM like Claude can pull from these docs and deal with the orchestration.
- Get immediate information, nonetheless it matches your finances. An AI visibility software saves the handbook work if it covers your language and market. If it would not, you may collect prompts your self, for instance with a Chrome add-on like ChatGPT Search.
- Rating the gaps with 4 questions. Are we invisible for this immediate, do folks ask one of these query, can we again up a solution with actual materials, and are opponents already there? Drop something you may’t assist with proof.
- Put the output the place your author already works. For me, that is Buffer’s Create area by the API, so the assessment and publishing step wants no entry to the automation in any respect.
Able to construct?
In case you’re taking Buffer’s API for a spin, we have got assets to get you shifting. Our developer docs cowl the GraphQL schema, auth stream, and quick-start examples. The Buffer MCP server docs stroll by plugging it into Claude or any MCP-compatible AI agent.
In case you want hands-on assist, our assist workforce is round, or you may be part of our Discord server and chat to different folks constructing with the API.
We would love to listen to about what you make. Discover us in Discord, or @buffer on all main social channels.
