A LinkedIn presence that learns from every post
Each cycle it scores how the last post actually performed, drafts fresh options from current industry news, recommends the one most likely to land, and waits for your approval before anything goes live.
- Schedule
- Runs on demand
- Integrations
- Anthropic ClaudeBrave SearchPostpeerSendGridWeb
The problem it solves
Posting consistently on LinkedIn is a treadmill: someone has to find something worth saying, write it in the company's voice, avoid repeating last month's topic, and decide which angle is worth publishing — usually on instinct, with no memory of what worked before. This workflow runs that loop for you and keeps the memory. It opens each cycle by grading the post it published last time against that post's real engagement, then pulls current compliance and fintech news, discards any subject already covered in the last thirty days, and drafts a small set of candidate posts — each tagged with a regulatory topic and grounded in your own positioning pages so the product references stay accurate. It then recommends one of those drafts — and whether that post should carry a link back to your site — as the combination to publish. You get those drafts by email with the recommendation highlighted, and you approve, edit, or skip. Only what you pick is published, and what you picked is recorded so the next cycle can score it. The drafts you skip are filed as cancelled records rather than discarded, so there is a trail of what was considered. Over time the recommendation moves toward the topics and treatments that actually earn engagement, instead of toward whoever argued loudest in the content meeting.
Who it's for
- Content and social marketers responsible for a steady LinkedIn cadence
- Marketing leads who want every post reviewed by a person before it publishes
- Fintech and compliance marketing teams that post against regulatory news
- Small marketing teams without the bandwidth to A/B test their own social content
How it works
Each step is configured, audited, and traceable — not code.
Questions
Does anything get published without me seeing it?
No. Every cycle stops at a review step: the candidate drafts are emailed to you with the recommended one highlighted, and nothing is scheduled until you select a draft. You can approve one as written, edit the body before it goes out, or skip the cycle entirely.
How does the recommendation get better over time?
Each run begins by grading the post from the previous cycle using that post's real engagement — clicks, shares, saves, comments and likes — and feeding the result back into the model that makes the recommendation. Because the workflow records which draft you actually published, not just which one it suggested, it learns from what really went out. The more cycles it runs, the more the recommendation reflects what performs for you.
How does it avoid repeating topics we've already posted about?
Before drafting, it pulls your posts from the last thirty days and compares each candidate news item against them. Anything that repeats the specific subject of a recent post is dropped, so a story that has already been covered doesn't come back around.
What systems does it connect to?
It searches the web for current industry news through Brave Search, reads and summarizes the articles it finds with Claude, schedules and publishes through Postpeer to your connected LinkedIn account, and sends the review email through SendGrid.
What happens to the drafts I don't pick?
They aren't thrown away. Each skipped draft is filed and then cancelled, leaving a record of what was considered in that cycle without any risk of it publishing later.
What if the post is rejected after it's submitted?
Publishing is verified rather than assumed. If the platform doesn't confirm the post, the run records the outcome as rejected instead of crediting it, and stops cleanly — so a failed publish is never reported as a successful one, and never distorts what the model learns.
Put this workflow to work
We'll walk your process end to end and show you what it looks like running in Arrow AIM — with a human in the loop and a complete audit trail.