Does this replace our CRM, or sit on top of it?

No. Sits on top. The agent reads from your CRM, transactional warehouse, and behavioral sources, runs the modeling, and pushes target lists back to your existing campaign tools. Nothing migrates.

What makes a segment from this agent different from one our current personalization stack already builds?

Most stacks segment by behavior or RFM and stop there. This agent runs ML churn, CLV, and recommender models per customer, then layers channel propensity and offer logic on top. The output is a per-customer action (channel, offer, discount level) bundled into a list.

How current is the data when the agent decides who's at risk?

The data layer refreshes on the cadence of your warehouse and event streams. The agent reads at prompt time. Churn scores reflect the latest available transactional and behavioral signal.

Customer Engagement Agent

Your personal customer engagement agent. Who’s at risk. Where to reach them.

Identify churn risk, basket headroom, and next-best offers per customer. Get a validated target list staged for push to your CRM in under 10 minutes.

Grounded in your transactional, behavioral, loyalty, and CRM data. Covers the entire B2B2C chain.

ISO 9001

ISO 27001

GDPR compliant

Book a live agent session See how it works

Your personal customer engagement agent.

Most of the time, the list is last quarter's.

You get the questions: “Who’s about to churn? Who has basket headroom? Which channel works for whom?”. And the data exists, but it lives in tools that don’t speak to each other and refresh on different cadences. Transactional history in one warehouse. Loyalty events in another. Behavioral signals in the CDP. Channel engagement in the campaign tool.

Campaigns get built on segments that were accurate six weeks ago, against churn risk that surfaced a month too late, with offers calibrated to averages instead of individuals.

That’s not how you grow CLV today.

Ask who's at risk. Get the target list, the offer, and the push.

The Customer Engagement Agent sits across CRM, transactional history, loyalty data, and behavioral signals.

It identifies who is at risk, who is high-value, who has basket headroom, and what specifically will move each one.

Get the right offer for the right channel at the right level of discount.

Customer engagement agent

Four specialists. One coherent target list.

Every prompt triggers the same pattern. An orchestrator picks the data sources, KPIs, and models. Four specialized sub-agents work the question in parallel. An advisory layer fuses results and validates them statistically.

You get the target list with offers, channels, and push staging included.

You

Decrease churn among our profitable customers this quarter.

Orchestrator agent

It personalizes CRM at scale, decodes loyalty and retention drivers, and automates targeting and content.
Routing to sub-agents

Profiler agent

Segmentation, RFM scoring, CLV modeling, audience definition

Recommender and basket agent

Per-customer next-best product, basket headroom, cross-sell logic

Retention and churn guard

Churn probability scoring, at-risk identification, intervention windows

Channel and activation optimizer

Channel propensity, offer calibration, discount logic, push staging