Use cases
Seven areas, one order of operations
The first four are foundations every agent reads from. The last three are what you switch on once they hold.
Data foundation
AI agents route, score and respond from CRM fields. If lifecycle, owner and industry mean different things to different teams, or records are duplicated and loosely associated, the AI scales the mess.
Permissions & seats
AI only sees what the permission model exposes. Different roles see different data, so outputs change by who asks, and missing paid seats block agents entirely.
Brand voice & knowledge
Missing knowledge means wrong answers; weak brand voice means off-brand messaging; sensitive files in a shared vault mean a customer can be shown salary bands.
Customer Agent
Resolves tier-1 tickets from your knowledge base and escalates to a human. Untested, it gives confident wrong answers and traps frustrated customers in loops.
Prospecting Agent
Drafts personalised outreach from CRM data. Misconfigured, it emails active deals, burns domain reputation and gets you blacklisted.
Content Agent
Remixes high-performing posts into multi-channel content. Low risk once brand voice is set, but unreviewed output erodes trust in the brand.
Governance & rollout
AI is not self-running. Without an owner, outcome metrics, documented automation and a phased rollout, momentum fades and broken workflows multiply quietly.