The problem this solves
AI sales tools degrade in a specific way: they keep producing output while quietly losing relevance. Prospecting drafts read generic because the CRM context behind them is thin, reps stop trusting suggestions after a few bad ones and revert to manual work, and nobody owns the question of whether the AI layer is actually creating pipeline. The license line item stays; the value quietly leaves.
How we work
The monthly loop has three parts. First, output review: we sample what Breeze and other AI-assisted features produced, from prospecting drafts to summaries and suggested actions, and grade it against what a good rep would have written. Patterns in the failures point at fixes, which are usually context problems rather than model problems.
Second, context and configuration work: enriching the CRM data the agents draw on, tightening instructions and enrollment criteria, and adjusting which steps stay automated versus which need a human pass. An agent without CRM context produces generic messages, so a large share of this work is making sure the context is there.
Third, adoption: short sessions with reps on where the AI output is reliable, where to edit, and where to override. Each cycle ends with a short report on usage, quality trend, and what we changed.
Deliverables
- Monthly AI output quality review with graded samples
- Tuned agent instructions, enrollment criteria, and automation boundaries
- CRM context improvements feeding the AI layer
- Rep sessions on working with AI-assisted tools effectively
- Usage and quality trend reporting, cycle over cycle