A fast-growing fintech startup had 400+ unused HubSpot properties, workflows that contradicted each other, and reports that nobody trusted. We tore it down and rebuilt it — data model, lifecycle stages, Breeze AI agents, and dashboards that actually reflect reality.
By the time of the engagement, this startup's HubSpot instance had grown organically for two years without any architectural oversight. Every new campaign or initiative had added properties, workflows, and lifecycle stage definitions — none of which were ever cleaned up or reconciled.
The consequences were significant. Sales reps didn't trust the CRM data, so they kept their own spreadsheets. Marketing couldn't accurately attribute revenue because the deal stages were meaningless. Leadership asked for a pipeline report and got three different answers depending on who ran it. The Breeze AI features they'd paid for were unconfigured and doing nothing.
The core problem wasn't just messiness — it was that the data model didn't reflect the actual sales process. Properties existed that no one could explain. Workflows ran in loops. Lifecycle stages had been redefined mid-year, so historical data was incomparable to current data.
The engagement ran in three phases over six weeks:
Phase 1 — Audit and architecture (week 1-2). Full inventory of all properties, workflows, lifecycle definitions, and integrations. Mapped the actual sales process against the CRM data model and identified every divergence. Produced a written architecture document: what the data model should look like, why, and what needed to be migrated vs. scrapped.
Phase 2 — Rebuild (weeks 2-5). Executed the rebuild against the approved architecture:
Phase 3 — Migration and training (week 5-6). Migrated historical data to the new structure where possible. Ran training sessions with the sales and marketing teams on the new conventions. Documented everything in a written playbook that lives in Notion.
Pipeline visibility went from essentially zero to 95% within the first week after cutover. Leadership could now run a forecast that everyone agreed on. The three-answer pipeline problem was gone.
Deal velocity improved 40% over the following quarter. The primary driver was clarity: reps now knew exactly what needed to happen to move a deal to the next stage. Previously, deals would sit in ambiguous stages for weeks because no one was sure what "Proposal" meant. With explicit entry criteria and automated reminders, deals moved.
The Breeze AI deal summary agent alone saved the sales team an estimated 45 minutes per rep per day — they no longer needed to manually write deal notes before calls because the AI had summarized the contact history, open tasks, and last interaction automatically.