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AI decisioning, head to head
Klaviyo agents run the campaign. Replenit is the signal.
Both call it autonomous, but they operate at different layers. This page compares them directly, on the one thing they claim in common: deciding what happens next for a customer.
Klaviyo is building the autonomous B2C CRM. Composer generates full campaigns, flows, and segments from a plain-language prompt on 14+ years of performance data, and Customer Agent resolves service requests across channels. It is genuinely strong. But an agent still runs a campaign a human reviews and approves, on top of a flat profile and event data model, and "which campaign, which variant" is a different question from "what does this one customer need next, and why."
Replenit answers the second question. Maestro, our AI CRM Manager, enriches your data into a relational, living memory of every customer, product, and brand, reasons over it to decide the next best action per individual, generates the message and the why, and owns the key retention workflows end to end. Klaviyo's roadmap points at agents that run campaigns autonomously; even then, an agent needs a signal telling it what each customer needs. Replenit is that signal, and today it already owns the retention decision.
Short answer: Klaviyo is an autonomous B2C CRM whose agents generate and run the campaigns a human approves. Replenit is the decision signal above the campaign: it reasons per customer, decides the next best action with the why, and owns the retention workflows.
AI decisioning, head to head
Klaviyo's agents generate and run the campaign your team approves. Replenit reasons per customer and decides the move. Here is exactly what each one does.
| Dimension | Klaviyo | ReplenitMaestro |
|---|---|---|
| What it is | An autonomous B2C CRM: data platform plus agents that generate and run campaigns and service | A decision engine that reasons per customer, then owns the retention workflow |
| What the agent decides | Which campaign, flow, and segment to build and optimize from your prompt | What this customer needs next: which workflow, which product, why now, in what tone |
| Unit of decision | The campaign, flow, and audience an agent generates for a marketer to approve | The individual customer, one reasoned decision at a time |
| Data model | Flat customer profiles and events, optimized for messaging and analytics | Relational Golden Records: customer, product, and brand memory with the joins a decision needs |
| Data enrichment | Unifies first-party data and predictive attributes across 350+ integrations | Enriches and synthesizes missing context into a living memory that keeps updating |
| Living memory and timing | Predicted attributes (next order date, CLV) inform when a flow fires | Living memory tracks lifecycle change: when a baby grows, it times the next size and refill |
| The "why" | Recommendations and predictions surface what performs; no interpretable per-customer reason | Explains why this customer, this action, right now, logged and auditable |
| Data needed to act | Predictive models and testing need history and volume to be meaningful | Minimum-dataset reasoning: decides confidently with as few as two purchases |
| Operating model | Human-in-the-loop: agents draft, recommend, and QA; nothing goes live without approval | A hire, not a tool: Maestro owns the workflow; you set strategy and guardrails |
| Scope of ownership | Owns the messaging campaign and service conversation you direct | Owns the retention lifecycle: replenishment, winback, cross-sell, churn |
| Where it sits | The system of engagement your sends run on | Above the stack: returns a Golden Decision Event that fires a Klaviyo flow |
| Net effect | A faster-built, on-brand campaign your team approves and ships | The per-customer decision made and executed for you, end to end |
When each one fits
The honest bottom line: these operate at different layers, and many teams run both. Klaviyo's agents generate and run the campaign; Replenit makes the individual decision that Klaviyo then executes. That is exactly the pattern behind Ovabalance's 340% jump in repeat revenue, Replenit's reasoning committing the decision, Klaviyo delivering it. The fastest way to see the difference on your own stack is a demo.
Feature by feature
Proof
These teams keep Klaviyo delivering downstream and let Replenit own the per-customer decision, the exact pattern this page argues for: reasoning decides the move, Klaviyo sends it.
Questions
They operate at different layers. Klaviyo is an autonomous B2C CRM: its agents (Composer, Customer Agent) generate and run the campaigns and service conversations your team approves. Replenit is the decision signal above the campaign: it reasons about each individual customer, decides the next best action and the why, and owns the retention workflow. Many teams run both, Replenit makes the decision, Klaviyo executes it.
The campaign vs the decision
Klaviyo runs the campaign. Replenit owns the decision.
Composer generates and runs the campaign your team approves. Replenit reasons its way to the right move for each individual customer, times it to their lifecycle, and hands it to Klaviyo to deliver. See the difference on your own customers: no rip-and-replace, live in weeks, and a hire that pays for itself (Ovabalance: +340% repeat revenue with Klaviyo).