I design operations
for growing teams

LJB's content and research work was going to keep eating hours as the team scaled, so instead of hiring around the bottleneck, I removed it. I built and run two production agents that operate on schedules and handle real business workflows: one plans, drafts, brand-edits, and publishes 5 social posts every Tuesday, pulling from a structured library of company messaging and practice guidelines, then routes each post into a colleague's review queue automatically. The other researches upcoming industry conferences for missing details like dates, deadlines, costs, and booth requirements, flags anything it can't verify instead of guessing, and only writes to our project tracker after I confirm the findings.

LJB's content and research work was going to keep eating hours as the team scaled, so instead of hiring around the bottleneck, I removed it. I built and run two production agents that operate on schedules and handle real business workflows: one plans, drafts, brand-edits, and publishes 5 social posts every Tuesday, pulling from a structured library of company messaging and practice guidelines, then routes each post into a colleague's review queue automatically. The other researches upcoming industry conferences for missing details like dates, deadlines, costs, and booth requirements, flags anything it can't verify instead of guessing, and only writes to our project tracker after I confirm the findings.

A backlog of unpublicized project wins meant marketing was leaving credibility on the table, so I built a pipeline to turn that backlog into a year of content instead of writing it one post at a time. I extracted all projects from our website and put them in an excel table with all key information. Then I had the AI create three versions of each post (standard, core values, and storytelling). Lastly, I sent them straight to our Monday.com board with the draft and a post date based on the timeline I gave it.

A backlog of unpublicized project wins meant marketing was leaving credibility on the table, so I built a pipeline to turn that backlog into a year of content instead of writing it one post at a time. I extracted all projects from our website and put them in an excel table with all key information. Then I had the AI create three versions of each post (standard, core values, and storytelling). Lastly, I sent them straight to our Monday.com board with the draft and a post date based on the timeline I gave it.

HubSpot's page builder was going to cap how the brand could look, so instead of accepting that ceiling or paying for a developer, I decided to build around the tool instead of inside it. Fixed layouts, limited flexibility, and a finished product that rarely matches what marketing actually envisioned — building the page with AI instead removed that ceiling and gave the team something cleaner, more polished, and fully on-brand.

HubSpot's page builder was going to cap how the brand could look, so instead of accepting that ceiling or paying for a developer, I decided to build around the tool instead of inside it. Fixed layouts, limited flexibility, and a finished product that rarely matches what marketing actually envisioned — building the page with AI instead removed that ceiling and gave the team something cleaner, more polished, and fully on-brand.

A 14-person GTM team was about to scale past the point where tribal knowledge works, so I got ahead of it before the breakdowns started. Leads came in and disappeared. There was no shared definition of a qualified lead, no routing, no reporting, and no way to know what happened after a handoff. I built the whole system that included lifecycle stages, MQL criteria, contact management, segmentation, automated workflows, and real-time reporting in HubSpot.

A 14-person GTM team was about to scale past the point where tribal knowledge works, so I got ahead of it before the breakdowns started. Leads came in and disappeared. There was no shared definition of a qualified lead, no routing, no reporting, and no way to know what happened after a handoff. I built the whole system that included lifecycle stages, MQL criteria, contact management, segmentation, automated workflows, and real-time reporting in HubSpot.

$3.3M in partnership revenue was one departure away from chaos, so I treated that as the real risk and built the infrastructure before it became one. The Dayton Dragons were managing it the way a lot of teams manage things when they're moving fast, across inboxes, individual memory, and spreadsheets nobody else could follow. With 60+ accounts and cross-functional execution involved, there was no single place where any of it lived. Activation workflows didn't exist and delivery was held together by different individuals. I built the infrastructure from scratch to develop centralized systems, delivery workflows, partner communications, and the engine to run it all.

$3.3M in partnership revenue was one departure away from chaos, so I treated that as the real risk and built the infrastructure before it became one. The Dayton Dragons were managing it the way a lot of teams manage things when they're moving fast, across inboxes, individual memory, and spreadsheets nobody else could follow. With 60+ accounts and cross-functional execution involved, there was no single place where any of it lived. Activation workflows didn't exist and delivery was held together by different individuals. I built the infrastructure from scratch to develop centralized systems, delivery workflows, partner communications, and the engine to run it all.