Problem
Customer-development work was producing meetings, leads, names, and follow-ups faster than the team could reliably track them by memory and scattered notes alone.
Product management proof
Building a CRM and Automation Layer
I built a CRM and automation layer so the team could keep up with a growing volume of meetings, leads, names, and follow-ups without relying solely on memory and scattered notes.
What I owned
I built the HubSpot, Notion, and n8n system that connected lead records, meeting context, follow-up obligations, and customer-learning signals.
Result
The team could see next actions, preserve relationship context, and follow up on business-development opportunities with less manual reconstruction.
Certain identifying details and artifacts have been omitted or generalized to preserve confidentiality.
The situation
The first thing I noticed was that customer-development momentum could disappear between tools. Relationship context was accumulating across conversations, notes, documents, and manual follow-ups, and the team needed a better way to keep track of it.
Pilot relationships needed a clearer path from first contact to follow-up, tailored proposal development, conversion, and structured learning.
The problem was not simply that there were too many tasks. The team needed a shared record of who we had spoken with, what they cared about, what should happen next, and which opportunities deserved more attention.
As the customer-development work matured, the operating question also changed. Volume was not enough. The team needed a way to turn conversations into structured learning about pains, objections, budget authority, persona fit, and which relationships were actually moving toward commitment.
I took the initiative to turn that context into a shared system instead of leaving it inside scattered notes and individual recall.
The approach
I designed a repeatable operating system across HubSpot, Notion, and n8n. The workflow connected relationship context, pipeline status, follow-up routines, and pilot materials so that customer-development work was easier to track and advance. I also built targeted automations for recurring tasks where structure and context could improve follow-up quality and reduce the chance that customer signal stayed trapped in individual memory.
In practice, I was building the CRM and automation layer behind the pipeline. The system had to preserve enough context for better follow-up without making the team maintain a heavy process for its own sake.
What I built
- a CRM-backed pipeline and lead-tracking workflow
- relationship-context records for more tailored outreach
- an AI-assisted partnership outreach generator that combines HubSpot deal data, Notion opportunity context, an LLM, and Gmail drafts
- a calendar-based workflow that prepares external meeting reminders and a daily summary
- an internal meeting reminder workflow that sent Discord reminders to the team
- an external meeting reminder workflow that sent Gmail reminder emails to meeting attendees
- a production lead-intake workflow that routes form submissions into HubSpot with Notion and Discord support
- customer-intelligence fields and follow-up routines that helped distinguish activity from validated learning
- reusable proposal and pilot materials
Why it matters
The work turned fragmented relationship management into a repeatable customer-development system. It demonstrates how I use lightweight automation to improve follow-through without losing the context needed for relationship-driven work.
That is the tradeoff I cared about: more structure, but not less humanity. Customer development only improves if the system helps people remember the specific reason a conversation mattered.
Operationally, the system made the team’s follow-up obligations visible. It was easier to see which conversations needed action, which context should shape the next message, and where the pipeline was depending on manual memory.
Result
The broader customer-development effort produced a more centralized pipeline, clearer follow-up discipline, and stronger visibility into which conversations were producing real customer intelligence or pilot signal. The automations supported the operating system behind that work; they should not be read as the sole cause of any commercial outcome.
What I learned
Customer-development automation is most useful when it preserves relationship context. The goal is not to automate every interaction. It is to make the right follow-up easier to execute at the right time.