- Sales / CRMIndustry
- Sales & RevOps teamsClient
- Machine LearningCategory
- Phased rolloutTime to impact
The challenge
- The data needed to sell more and keep customers already sits in the ERP and CRM, but it is static.
- Reps do not know who to prospect next, churn surfaces too late to act on, and outreach is rarely tied to how satisfied a customer actually is.
Turns the data already in your ERP and CRM into prospecting signals, churn risk and CSAT-aligned engagement.
An intelligence layer that plugs into existing ERPs and CRMs to surface the next best prospects, flag accounts at risk of churning, and steer customer engagement around real satisfaction signals, without replacing the systems teams already use.
What we built
- 01Connects to existing ERP and CRM systems instead of replacing them.
- 02Surfaces sales-prospecting signals from account and transaction data.
- 03Predicts which customers are at risk of churning, early enough to act.
- 04Aligns customer engagement with CSAT so outreach matches real satisfaction.
How it works
What it can do
ERP/CRM prospecting
Surfaces the next best prospects from data already in your systems.
Churn prediction
Flags accounts at risk before they leave.
CSAT-aligned engagement
Ties outreach to real customer-satisfaction signals.
Works in your stack
Plugs into existing ERP and CRM tools, no rip-and-replace.
Who stays in the loop
The call the system deliberately does not take, and what the person is given to take it with.
An intelligence layer that plugs into existing ERPs and CRMs to surface the next best prospects
The account owner
Decideswhich at-risk account to work, and how
Seesthe churn score with the signals behind it, and the prospecting list in priority order
The payoff
What is different once it is running. No invented numbers: these are the changes the work was built to make.
The signals to sell more and churn less already sit in your ERP and CRM. This puts them to work instead of leaving them to sit there.
Reps get a next-best-prospect list from data you already own.
At-risk accounts surface early enough to save.
Engagement follows real satisfaction, not guesswork.
Built with
The layers this runs on, from what comes in to what it plugs into.
- Model layer
- ML models
- Runs on
- Agentic workflow
- Connects to
- ERP / CRM integration
Common questions
The same answers as above, written out.
An intelligence layer that plugs into existing ERPs and CRMs to surface the next best prospects, flag accounts at risk of churning, and steer customer engagement around real satisfaction signals, without replacing the systems teams already use.
The data needed to sell more and keep customers already sits in the ERP and CRM, but it is static. Reps do not know who to prospect next, churn surfaces too late to act on, and outreach is rarely tied to how satisfied a customer actually is.
Connect the existing ERP and CRM. Models score prospects and churn risk from the data. Reps get prioritised actions and at-risk alerts.
The account owner decides which at-risk account to work, and how. They see the churn score with the signals behind it, and the prospecting list in priority order.
Model layer: ML models. Runs on: Agentic workflow. Connects to: ERP / CRM integration.
Reps get a next-best-prospect list from data you already own. At-risk accounts surface early enough to save. Engagement follows real satisfaction, not guesswork.
Where this fits
The practice it belongs to, how an engagement like it runs, and what we have written about the subject.
- Sales
- CRM
- Machine learning
- Retention
- Sales / CRM
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