Sales / Account Growth Live
Offer Personalization
Describe the customer and get two or three bundles built for them specifically, each with what is in it, what it costs, and who inside the account it is aimed at.
- Run on demand
- ~5 credits per run
- v1.0.0
What does Offer Personalization do?
Offer Personalization is a production AI agent in the sales section of the Leverge agent store, built for the account growth process. Describe the customer and get two or three bundles built for them specifically, each with what is in it, what it costs, and who inside the account it is aimed at. It runs on demand, works through 2 steps and returns 4 outputs, including bundles compared.
What it needs
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The customer
Who they are, what they already have, what they have asked about, and who the buyers are.
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Bundle constraints
What it does
- Building the bundles
- Writing how to position them
What you get back
- Bundles compared
- What is in each bundle
- How to position them
- Assumptions to check
After each run it asks: “Would you put these bundles in front of the customer?”
When it runs
Run on demand
Oversight
Runs under scoped, least-privilege credentials with every action written to an audit log. Anything that moves money, alters a contract or reaches a customer requires human approval before it executes.
Account Growth
Other agents in account growth
Pipeline, proposals, renewals and the CRM hygiene underneath them
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Churn Signal Detection
Upload account health data and get the accounts showing churn signals, what each signal actually is, and which ones are an expansion opening in disguise.
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Customer Segmentation
Upload your customer base and get it divided into segments that emerge from the data, each with what defines it, what it is worth, and the one motion that fits it.
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Inquiry Triage
Answer the routine questions that reach a sales inbox from your own approved material, and route everything else to a person rather than guessing.
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Missing Data Collection
Work out which missing customer fields are actually worth chasing, who would know each one, and draft the request — instead of sending a blanket form.
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Objection Early Warning
Read a customer message for the objection underneath it — what is actually being raised, how serious it is, and the response your own material supports.
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Post-Proposal Engagement Read
Upload what happened after the proposal went out and get an honest read on intent — which signals mean something, which mean nothing, and what the silence actually tells you.
Next Step
Deploy Offer Personalization, or adapt it
It runs as-is against the inputs above. Most deployments diverge — a different source system, a different tolerance, a different approval path. A 30-minute technical call establishes which.