Unlocking a $4.8M Pipeline: An AI-Driven RevOps Rebuild Across Six Revenue Lines
Cure manages six distinct, thriving business lines through a single HubSpot ecosystem. As the business scaled rapidly, their data architecture struggled to keep up, creating operational blind spots. Using our custom AI-driven methodology, we rebuilt their revenue operations from the ground up, auditing massive datasets, programmatically restructuring the architecture in a live environment, and ultimately activating a $4.8M sales pipeline that was hidden in the data.
- $4.8M
- Pipeline activated
- 6
- Revenue lines unified
- 14
- Automated workflows built
About Cure
Health Tech
A premier New York health-innovation campus offering dynamic memberships, event space, and a collaboration residency, alongside a proprietary data product (CII) sold to major US biomedical research institutions.
Visit Cure↗The structural growing pains of rapid expansion
Cure operates a sophisticated business model, running six different revenue streams within a single HubSpot environment.
Rapid growth is exactly what a business wants, but it often outpaces the infrastructure supporting it. Over time, Cure's CRM architecture expanded organically. This led to overlapping data structures, for instance parallel properties capturing the same vital information across different departments. Across a sprawling database of 400,000 contacts, these structural overlaps created reporting blind spots, slowed down the sales team, and pushed the account toward unnecessary software billing overages.
The stakes for fixing this were exceptionally high. The client's HubSpot tier lacked a "sandbox" testing environment. This meant any architectural overhaul, massive data migration, or property consolidation had to be executed directly in the live production environment, without dropping a single record or disrupting active, high-value sales cycles.
They didn't just need a consultant; they needed an engineering-grade operation.
An AI-driven, programmatic rebuild (with zero downtime)
To safely untangle and rebuild a live architecture of this scale, manual data entry and guesswork are not options. We deployed custom AI agents and programmatic scripts to do the heavy lifting with surgical precision.
- Deep AI data analysis: We leveraged AI to ingest and analyse all 400,000 contact records. The system instantly mapped property usage, identified data gaps, and surfaced redundancies that a human audit would have taken months to find.
- Strategic architecture: Before writing a single line of code, we workshopped with the leader of each revenue line to establish absolute business rules. We designed the system to perfectly mirror and enforce their actual sales processes, rather than forcing the team to adapt to the software.
- Programmatic migration: Instead of risky manual updates, we used AI workflows to programmatically generate necessary new canonical properties and safely migrate legacy values into clean, consolidated fields.
- Failsafe deployment: Every new automation, workflow, and data mutation was dry-run and verified against small data batches before full deployment. This rigorous testing protocol ensured a flawless transition with zero data loss in the live environment.
- 01
Audit
A read-only survey of every object, property, pipeline and owner before anything was touched. It found the opposite of the brief: the schema was built, the discipline wasn't.
- 02
Workshop
Working sessions with the owner of each revenue line, business rules first, software second. This is where rules like “only the 303 ranked institutions ever generate a deal” were settled.
- 03
Map
The canonical property set drafted onto a shared FigJam board, with reuse, create or retire decided per field. The client adopted the board as his own planning surface.
- 04
Spec
Build-packs and workflow specs reconciled against the live schema before a line was built, with two living PRDs carrying numbered decisions and open questions.
- 05
Test
Workflows built disabled and verified; every data mutation dry-run, then canaried on a small live batch. One “high confidence” match tier came back 60% wrong, caught before a single record was written.
- 06
Apply & verify
Full production run, then before/after counts and a named reversal path, written down. 106 dated execution summaries across the engagement.
Tools we worked in
HubSpot
Python
Claude CodeRevenue clarity and operational scale
Cure now operates on a streamlined, heavily automated CRM that acts as a true growth engine rather than a mere data repository.
By leveraging AI, we audited 156 custom properties, programmatically generated purpose-built new fields, and safely merged overlaps. We sanitised the entire database, isolating unmarketable contacts and permanently eliminating a looming 50,000-contact billing penalty, saving immediate overhead costs.
Beyond pristine data hygiene, we built 14 automated sales and marketing workflows, created dedicated deal pipelines for their institutional products, and deployed customised dashboards and reports to give leadership total visibility into every revenue line. Cure successfully modernised their operations with a fully documented, scalable system, and the partnership continues today on a monthly advisory retainer.
- $4.8M
- Pipeline activated
- 6
- Revenue lines unified
- 14
- Automated workflows built
Structured 80 high-value institutional deals at a $60k flat average value, giving the sales team immediate, actionable targets.
Each business stream now operates with its own clear data structure and precise reporting dashboards, while sharing one cohesive portal.
Automated sales and marketing workflows, alongside centralised reporting, live dashboards, and AI-driven data migrations across 400,000 records in a live environment.