Your LinkedIn content end to end, design included, plus the newsletter, the article that ranks and the page ChatGPT cites when someone asks about your category. 12 posts a month per profile.
The difference between someone writing your posts and having a content engine is that the engine learns. These are the parts that make it learn.
Deep research and pillars
It researches your company and defines content pillars per profile, each tied to a funnel stage, plus the hook strategy.
You don’t stare at an empty calendar wondering what to write about this week.
The hook strategy is validated once, not post by post
The learned voice
It reads the profile’s last 15 posts and extracts the real writing patterns: lowercase usage, sentence length, punctuation rhythm, hook shape.
You don’t read a draft that sounds like generic LinkedIn and have to rewrite the whole thing.
Calibrated against your actual profile, not a "write like a founder" prompt
Idea bank
Competitor posts, YouTube videos, blogs, CSVs, PDFs and loose text go in, and ideas tied to your pillars come out.
You don’t send links over WhatsApp hoping someone turns them into something.
Six different source types, all into the same queue
Design with your brand book
It extracts colors, backgrounds, typefaces, logos and rules from your brand book, in PDF or HTML, and designs every piece with them.
You don’t hire a separate designer or explain the brand kit every time.
8 images and 4 carousels a month per profile, in your identity
Learned preferences
It compares the draft we left against the one you approved after editing, understands the difference and writes it down as a preference.
You don’t correct the same thing twice. Review shrinks on its own.
There’s an equivalent loop for design, not only for text
Publishing and scheduling
You approve in the platform and it publishes or schedules straight through LinkedIn OAuth, on the right profile.
You don’t copy and paste at nine in the morning or hand the post off to anyone.
Multi-profile: the founder, the head of sales and whoever else
Synced metrics
It pulls impressions, reactions and engagement rate for every published piece and feeds them back into the idea bank.
You don’t export a LinkedIn CSV to find out what worked.
What works comes back as input, not as an anecdote
Newsletter, blog and AEO
The same material ships as a newsletter and as an article optimized to be found and to be cited by the models: a direct answer up front, structured data (JSON-LD) and content that can be quoted.
You don’t write the same thing three times for three channels.
One file, two pieces: the one that lands in the inbox and the one that ranks
They aren’t mockups or client samples. They’re ours, they’re published on LinkedIn, and the copy, the design and the scheduling all came out of the process you just read.
ZalesMachine · LinkedIn
Real average of the last 10 published pieces: 1,160 impressions and 32 reactions.
It’s the same process we use for our own content. The pieces further down this page came out of it.
Research on your company, your category and who is already talking about this in your market.
Each profile’s pillars, each tied to a funnel stage. The founder and the head of sales don’t post the same thing.
How every piece opens. Validated with you once, then it runs on its own.
We load the sources: competitor posts, videos, blogs, CSVs, PDFs, loose text. Everything lands in the same queue tied to a pillar.
Every piece is written following your profile’s real patterns, not a generic tone.
The image or carousel is built with your brand kit: your colors, your typefaces, your logos and your rules.
Internal review, then yours, on a kanban board. What you edit comes back as a learned preference.
It publishes or schedules through LinkedIn OAuth, on the right profile.
Impressions, reactions and engagement for every piece come back into the platform and feed the idea bank.
The same prospects you message are the ones reading you every week. We measured it with a single variable changed.
2.4×
more meetings per rep, with the same ICP and the same 1,600 invitations.
Layer 01
Outbound
You message 1,600 prospects from your ICP
Every accepted connection adds a feed impression. The network grows with prospects, not with random followers.
The first thing a prospect does is check the profile. With content they see judgment; without it, spam.
Layer 02
Content
Those same prospects read you every week
Acceptance rate
+33%
Reply rate
+29%
Meetings per rep
2.4×
How it was measured
Same company, same 4 reps, same ICP, same 1,600 invitations. Single variable: turning layer 02 on.
The finding
If the effect were only funnel math, meetings would rise 1.72×. They rose 2.4×. The rest is conversation quality: the prospect already knows who you’re when they reply.
The work that scales isn’t the writing. It’s learning each person’s voice and calibrating their brand kit. A new profile starts from zero, one more post doesn’t.
$500/mo
The founder, or whoever is the face of the company.
$1,400/mo
When the sales team has to be present too.
Monthly price per profile. If you hire outbound to sell into the United States, content is included in both plans: there the prospect checks your profile before accepting the invite, so it isn’t an extra, it’s what separates a credible invitation from one that looks like spam.
The platform reads your last 15 posts and extracts the real patterns of how you write: whether you use lowercase, how long your sentences run, how you punctuate, how you open. The draft follows those patterns. And every time you edit something before approving it, that difference is stored as a preference and doesn’t happen again.
Yes, and that’s deliberate: it goes out from your profile, under your name. What changes over time is how much you have to correct, because every edit of yours feeds the system. Approval is a kanban, not an email chain.
The platform. It extracts colors, backgrounds, typefaces, logos and rules from your brand book, in PDF or HTML, and builds every image and carousel with them. There’s no designer to re-explain the brand to every time, and design has its own learned-preferences loop.
From a bank loaded with mixed sources: your competitors’ posts, YouTube videos, articles, a CSV, a PDF, a text you send us. Everything enters the same queue tied to one of your pillars. And what worked comes back in: the metrics of every published piece feed the bank.
Both, and a third one growing fast: the same material ships as a newsletter and as an article structured to be found and to be cited by the models, with a direct answer up front and structured data (JSON-LD). More and more people ask a chat instead of searching, and there the question isn’t whether you rank, it’s whether you get cited.
Because the heavy work isn’t writing one more post: it’s learning a new person’s voice and calibrating their design. A profile starts from zero, an extra post runs on something already built.
You’re looking at it. The pieces on this page are ours, they came out of the platform and they’re published on LinkedIn. They aren’t mockups.
It helps in a different way. If you already have someone writing, what this adds is the part that doesn’t scale by hand: design with a brand kit, multi-profile scheduling, metrics feeding back into the idea bank, and the piece for SEO and AEO. We work that out on the call before quoting anything.
This is why we work with enterprise and VC-backed companies.
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