A governed AI agent model turned intent signals into personalized outreach at scale
A marketing organization built a governed intake to execution model for AI agents, turning scattered buyer signals into timely, personalized outreach across every go to market motion.
The Opportunity
The marketing team had buyer intent signals scattered across systems such as CRM activity, intent data, and account context, but no consistent way to turn them into timely, relevant outreach.
Good signals went stale before a rep could act on them, and there was no single, governed way to scale AI driven marketing actions without losing control over what agents were allowed to do.
The Transformation
A layered agent model was built to turn raw signals into governed, personalized action, with every new use case triaged, prioritized, and built through the same repeatable pipeline.
Data Foundation Agent
A foundation agent ingests CRM activity and intent signals in under a minute and turns them into a live, enriched context store. It removes the stale data delay that used to sit between a signal and a response, and gives every other agent a shared, up to date picture of each account.
Core Recommendation Agent
A recommendation agent assigns the single best next action per contact, ranks it against a marketer controlled action catalog, and scores it using a multi factor model that keeps learning from outcomes. It is what turns fresh signals into a specific, relevant next step, at scale.
Guardrails, Threading & Delivery Agents
Three more agents: one to route all AI inference through a governed gateway, one to manage multi turn email conversations with human escalation built in, and one to handle delivery across email and CRM tasks. Nothing goes live without passing through the same triage process.
From new intent signal to a scored, actionable recommendation
Lift in qualified conversation rate from timelier, targeted outreach
Pipeline influenced in the first two quarters of rollout
Reduction in manual campaign coordination effort