The problem this solves
The data is there, enriched companies, engagement history, support records, and nothing uses it. AI agents produce generic output because nobody connected them to context. Marketing cannot see service history, sales cannot see product signals, and the expensive unified CRM behaves like three separate databases with one login screen.
How we work
We start with an activation map: which data you have, which decisions and actions it should drive, and where the gap is. That turns 'we should use our data more' into a concrete list of scoring inputs, segment definitions, agent context sources, and cross-hub triggers.
Then we wire it. Breeze agents and AI features get fed deliberately, because an AI agent working from an empty or stale CRM writes messages with no context from your actual relationship; context preparation is most of the value. Scoring and lists start consuming enrichment and engagement fields. And cross-hub automation makes the platform act like one system: a spike of support tickets flags the renewal deal, product engagement signals reach the account owner, marketing suppresses accounts in active escalation.
The outcome is data with a job: every significant property either drives an action or is on a list to be retired.
Deliverables
- Activation map connecting data to decisions and actions
- Context feeding setup for Breeze agents and AI features
- Scoring and segmentation built on enriched and behavioral data
- Cross-hub automation: service, sales, and marketing triggers
- Suppression and safety rules preventing tone-deaf outreach
- Measurement view of which activations fire and what they produce