What the engines actually ship
Six posts from the approved LinkedIn queue, exactly as they will run: agent-made product videos and templated cards, captions clean, the product link riding in the first comment.
“Why did Day-7 retention drop 4.2%?” Your dashboard shows you the drop. It almost never tells you why.
So you go hunting. And the answer is usually hiding in one cohort or one broken step that a top-line chart will never surface on its own.
Ask ThinkingAI the same question in plain English. The agent does the digging through your funnels and cohorts, finds where users fell off, and drafts a likely cause with a fix worth testing.
You read the evidence and decide whether to ship it.
Every dashboard you trust is downstream of your tracking. And tracking quietly rots: a rename here, a missed property there, and a report is wrong for weeks before anyone notices.
The Data Collection Agent instruments and validates your events against your tracking plan, and catches breaks before they reach a report.
Your team stays in charge of the plan. The agent does the grunt work of keeping it honest.
Your team has jobs no vendor ever built an agent for. The weekly report nobody wants to write. The check that only one person knows how to run.
Build your own instead. Compose it from your own models, your MCP servers, and your Skills library, and point it at the job your team actually owns.
It runs on your rules, inside your own platform, and it still asks before it acts.
Your growth stack shouldn't require ten tools, five dashboards, and hours of manual work.
ThinkingAI brings analytics, attribution, audience intelligence, LiveOps, automation, monitoring, data pipelines, and real-time decision-making into one intelligent platform.
Fewer silos. Lower costs. Faster decisions. Autonomous growth, 24/7.
A weighted rotation, so the queue never runs dry
Eight content types feed the LinkedIn calendar, and each type is its own workstream with its own inventory and its own agent. A weighted rotation draws from them: roughly 35% education and POV as the evergreen backbone, 25% thought leadership repurposed from the blog plan, 20% feature and product posts, and the rest data cards, polls and commentary. Triggered types take a slot when fresh output lands; the evergreen backbone fills the gaps. Four to five posts a week, and no two weeks look the same.
Only approved posts enter the queue; every one previews exactly as it will read in the feed before I approve itFrom two or three posts a week to a calendar that stays full
The channel used to run at roughly two to three posts a week, and it ran on whatever someone had time to make. It now sits at about five scheduled posts a week, with unscheduled ones on top, and the coverage widened at the same time: product capability, brand story, blog, thought leadership, events and webinars all have a lane.
Two things made that possible. The first is that the other engines became the ammunition: once the ebook, blog and product-update workflows were producing on their own, the social calendar had something real to draw from every week. The second is a capability we simply did not have before, product-related posts, which needed motion. I pointed the video pipeline at it, reusing the same reference-frame technique and the Remotion and HyperFrame rendering path, so a product capability can now ship as a short video instead of waiting for a production slot.
The last piece is distribution inside the company. A published post lands in an internal launchpad where anyone can repost it or drop a prepared comment, copy and paste, no drafting required. Employee amplification usually dies from friction, and this removes most of it.
Coverage now: product capability · brand story · blog · thought leadership · events · webinars, plus unscheduled posts750 items in, five to eight out, two human gates
Each week a deterministic script harvests roughly 750 items and tags the ~280 with links that actually open. Three sector agents cluster and select 20 to 30 by reading titles and summaries. The script then fetches every selected URL, confirms it opens, and extracts the full article text, so the editor agent worth-scores real articles rather than headlines and picks the final five to eight. I edit the run-sheet and choose the POV at gate one; writers and an assembler produce the issue; I review and send at gate two. Item URLs pass through the pipeline verbatim from the source list, which leaves the model no opening to invent a link.
The finishing pass includes an AI-tell cleaner that kills em dashes and stock AI phrasing before anything reaches meOne POV per issue, one judgment per item
Every issue is built around a single point of view that I pick at the run-sheet stage. Issue 001's was “agent security is an observability problem,” and the lead essay, the pull quote, and the closing blog pick all argue it. The essay is a 120 to 220 word beat written in the house voice, reacting to one hook from that week's harvest.
Each news item is a linked headline plus a one-or-two-line house take. The rule for the take: add a judgment the source itself did not make. A survey becomes “an even split is a distribution, and the average is hiding it.” A benchmark becomes “the reusable part is the method, and the winner will change.”
The layout is locked and Outlook-safe: 600px table layout, inline CSS, a VML roundrect so the button stays rounded in Outlook, and a visible Read-more link on every item because hover states do not exist there. The assembler outputs this exact HTML, paste-ready for HubSpot, and I send it after the final read.
Serif for the essay, sans for the news, one accent color, and the real logo on white: the template is a written spec every issue inherits
DeepSeek
Kimi K3
ChatGPT
Claude
Gemini image
Ahrefs
HubSpot