Aampe at CRMC 2026

Turn every customer interaction into a smarter decision

Meet Aampe at CRMC to see how agentic AI helps CRM and lifecycle teams decide what to say, when to send, and which customer action to take next.

Aampe is trusted by world’s most forward thinking companies

Aampe at CRMC 2026

Turn every customer interaction into a smarter decision

Meet Aampe at CRMC to see how agentic AI helps CRM and lifecycle teams decide what to say, when to send, and which customer action to take next.

Aampe is trusted by world’s most forward thinking companies

Agentic decisioning for retail marketers ready to move past static campaigns

Aampe helps teams replace one-size-fits-most journeys with per-customer agents that learn from every message, click, purchase, unsubscribe, and pause. Each interaction becomes a signal that improves the next decision.

14–43

Relative GMV Lift

Fewer Unsubscribes

Lift Sustained Over Time

150

M+

User-Scale Deployment

hrs

Completed Integration & Testing

Based on randomized controlled trials and longitudinal holdout studies submitted, presented, and accepted at RecSys 2025, ECIR 2026, UMAP 2026. Results vary by use case.

How it works

How Aampe helps CRM teams make better decisions for every customer

Aampe fits into your current data and engagement stack, learns from real behavior, and helps your team improve lifecycle performance without rebuilding every campaign by hand.

Define goals and guardrails

Set your business goals, brand boundaries, and operating rules you want agents to work within.

Build your content + label strategy

Work with Aampe to map your content strategy, define your labels — like value proposition, tone, and offer — and create the semantic learning vocabulary agents will use across messages.

Connect your data and channels

Stream event data into Aampe and connect your engagement platforms so agents can learn from behavior and activate decisions where your team already works.

Upload, create, and organize content

Bring in existing content, use Relay to generate new variations, and structure everything so agents can learn from meaning, not just message IDs.

Agents decide the next best action for each user — and improve continuously

Each customer gets a dedicated agent that chooses the next best action — what to say, how to say it, when to send it, and where — then gets smarter with every interaction.

Define goals and guardrails

Set your business goals, brand boundaries, and operating rules you want agents to work within.

Define goals and guardrails

Work with Aampe to map your content strategy, define your labels — like value proposition, tone, and offer — and create the semantic learning vocabulary agents will use across messages.

Connect your data and channels

Stream event data into Aampe and connect your engagement platforms so agents can learn from behavior and activate decisions where your team already works.

Upload, create, and organize content

Bring in existing content, use Relay to generate new variations, and structure everything so agents can learn from meaning, not just message IDs.

Agents decide the next best action for each user — and improve continuously

Each customer gets a dedicated agent that chooses the next best action — what to say, how to say it, when to send it, and where — then gets smarter with every interaction.

Define goals and guardrails

Set your business goals, brand boundaries, and operating rules you want agents to work within.

Define goals and guardrails

Work with Aampe to map your content strategy, define your labels — like value proposition, tone, and offer — and create the semantic learning vocabulary agents will use across messages.

Connect your data and channels

Stream event data into Aampe and connect your engagement platforms so agents can learn from behavior and activate decisions where your team already works.

Upload, create, and organize content

Bring in existing content, use Relay to generate new variations, and structure everything so agents can learn from meaning, not just message IDs.

Agents decide the next best action for each user — and improve continuously

Each customer gets a dedicated agent that chooses the next best action — what to say, how to say it, when to send it, and where — then gets smarter with every interaction.

Customer Stories

How teams worldwide turn static campaigns into continuous learning

Usecases/Features

What makes Aampe different from typical AI Decisioning

Customer Speak

What our customers are saying about using Aampe

The Evolution of Personalization

The shift from Automation to Autonomy

Personalization within customer engagement has been upended countless times over the past few decades - from rule-based broadcast systems to AI decisioning.

But “AI decisioning” still optimizes predefined campaigns and workflow logic. Aampe represents the next step: an agentic intelligence layer built for continuous learning at the level of the individual customer.

Pre-digital

Rule-based

Batch and broadcast

Testing Era

a/b testing

Hypothesis-driven experimentation

Optimization

Bandits and MAB

Adaptive optimization on averages

ML Era

ML segmentation

Predictive audiences and model-based targeting

AI Decisioning

Automated decisioning

Offer and experiment automation

Agentic Era

Aampe Continuous

Intelligence Layer

Pre-digital

Rule-based

Batch and broadcast

Testing Era

a/b testing

Hypothesis-driven experimentation

Optimization

Bandits and MAB

Adaptive optimization on averages

ML Era

ML segmentation

Predictive audiences and model-based targeting

AI Decisioning

Automated decisioning

Offer and experiment automation

Agentic Era

Aampe Continuous

Intelligence Layer

Pre-digital

Rule-based

Batch and broadcast

Testing Era

a/b testing

Hypothesis-driven experimentation

Optimization

Bandits and MAB

Adaptive optimization on averages

ML Era

ML segmentation

Predictive audiences and model-based targeting

AI Decisioning

Automated decisioning

Offer and experiment automation

Agentic Era

Aampe Continuous

Intelligence Layer

It’s time to move from managing campaigns to designing growth systems: fewer static journeys, more continuous learning.

Integrations

Aampe-up your existing stack

Aampe is simple, enterprise-ready, and integrates seamlessly—no long setup required

Data Sources

Customer Data Platforms

⋯

Data Warehouse & Storage

⋯

CONTENT MANAGEMENT SYSTEMS

⋯

Recommendation Engines

⋯

Stream Ingestion API

Batch Ingestion Connectors

Realtime Content Resolution

Agentic Infrastructure

Agentic

Infrastructure

Agentic Infrastructure

Action Queues

Action

Queues

Action Queues

Content API

Content

API

Content API

Agentic Decision Data

Agentic Actions, Content & Data

Delivery Infrastructure

⋯

Engagement Tools

⋯

Observability & Insights

⋯

In-App Tools & Native Surfaces

⋯

FAQ

Your Questions, Answered

Does Aampe require static segmentation?

No. Aampe uses semantic content labels and per-user learning rather than relying on broad predefined buckets.

Does Aampe just optimize for clicks?

No. The internal materials position Aampe as learning across multiple horizons and using reward functions to connect short-term signals with longer-term outcomes such as retention, revenue timing, and sustained engagement.

How does Aampe learn?

Aampe learns from real user behavior using tagged content, reward signals, abstraction layers, social calibration, and next-best-action policy selection to update what each agent believes from real user behavior.

How is Aampe different from AI decisioning tools?

The core difference is the unit of learning. Most AI decisioning tools optimize campaigns, workflows, or segments. Aampe is designed to learn at the level of the individual person and carry that learning forward across multiple channels and surfaces in the product and lifecycle experience.

What is Aampe?

Aampe is an agentic intelligence layer for customer engagement. Instead of optimizing isolated campaigns or segments, it gives each customer a dedicated AI agent that learns continuously from interaction over time to deliver more personalized experiences.