A managing partner's LinkedIn now runs itself.
Six hours a week of content creation became a five-minute weekly review. Twenty posts a month publish on schedule, in the partner's voice, on topics her audience actually cares about. The partner kept her billable hours.
The SEC just dropped its disclosure-rule update, and most M&A teams are reading the wrong half of it.
Here's the part that actually changes deal mechanics for funds under $500M…
Three takeaways for partners working diligence this quarter:
20
Posts / month
5 min
Weekly review
6 hrs
Hours back / week
94%
Voice match
Six hours a week. Or zero posts a month.
The managing partner of a mid-size law firm needed a LinkedIn presence. Thought leadership, regulatory commentary, the kind of posts that build credibility one quarter at a time.
Producing that content took six hours a week. Either she wrote it herself and lost the hours, or her team wrote it and the voice came out flat. The cadence collapsed: three posts in a week, then nothing for a month.
The agency managing her marketing needed a system that worked for this partner and could scale to every executive on their roster.
Source monitoring
Industry publications, regulatory feeds, court rulings, peer-firm posts. The system reads what the partner reads — every morning, before the partner has finished coffee.
Relevance scoring
Each item gets a score against the partner's beat. SEC ruling on disclosure — 9. Generic legal-tech announcement — 2. The 9s become draft candidates. The 2s never reach the partner.
hook The SEC just dropped its disclosure rule, and most M&A teams are reading the wrong half of it.
contrarian Most teams will spend Q2 on disclosure templates. The bigger shift is in deal mechanics for funds under $500M.
listicle Three things to flag in diligence this quarter: 1. revised timing windows, 2. expanded MNPI scope, 3. signing-day liquidity reps.
Voice-calibrated draft
Using the partner's prior posts as reference, the system drafts a take. Direct sentences. The partner's signature contrarian angle. The specific examples she'd reach for.
Five-minute review
Drafts land in Airtable. The partner scans on her phone over coffee. Edit one sentence, approve, schedule. Five minutes for the week's queue.
The SEC just dropped its disclosure rule, and most M&A teams are reading the wrong half of it…
Schedule + publish
Approved posts publish on schedule, twice a week. Live on LinkedIn within seconds. Engagement reactions come back into the dashboard so the next batch knows what worked.
Five stages. One post in voice.
n8n underneath, Claude in the middle, Airtable on the surface, LinkedIn at the end. The next five panels are the stages, in order. Scroll to watch each one.
The SEC has issued an important update to its disclosure rules. This update will impact M&A teams across various sectors.
Companies should review the new requirements carefully and update their compliance procedures. It is essential to stay informed about regulatory changes.
Best practices include consulting with legal counsel, training your team on the new rules, and updating your disclosure documents accordingly.
We recommend a thorough review process to ensure compliance.
A standard prompt produces correct, hedged, interchangeable copy. The kind of post that scrolls past a feed without landing.
Same news. Two drafts. Toggle to see.
LinkedIn is full of AI content that reads like AI content. Hedged, safe, interchangeable. For this system to work, every post had to sound like the partner wrote it.
The widget below is the same news event drafted two ways. Toggle between Generic AI and In Voice to see what specifically the system calibrates for. Same model. Different research, different prompt, different result.
Five-minute review. Twenty posts. Same partner.
Drafts land in Airtable. The partner scans on her phone over coffee. Edits one sentence, approves the rest, schedules the week. Five minutes total.
The cadence below is the same partner’s posting rhythm before and after the system shipped. Use the toggle to compare.
05 / The lesson
Voice was the hard part.
The build was easy. APIs, Airtable, n8n. The model wasn't the constraint either. The constraint was: does this post sound like the partner, or does it sound like an AI pretending to be the partner.
Most automated thought leadership reads as automated. Hedged, generic, allergic to a real opinion. We solved that with a calibration layer: prior posts as voice reference, named beats for hooks and stance, banned phrases that signal AI authorship.
The technical build doesn’t need to be complicated. It needs to not fail. The value lives in the layer that makes the output indistinguishable from human work.
Next step