What is Aampe?

Aampe provides agentic infrastructure for real-time personalization and continuous experimentation. It assigns a dedicated agent to each user, runs controlled parallel experiments, and adapts messaging at the individual level. Features include automated multivariate testing, continuous feature engineering via semantic abstraction, causal distributions for counterfactual policy simulation, and a library of content variants.

Lifecycle marketing, product, and data science teams can use Aampe to optimize engagement, identify drivers of customer behavior, and accelerate product decisions without manual model building. Integrations cover customer data platforms, data warehouses, CMS, recommendation engines, and real-time ingestion APIs to deliver content and actions across channels.

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Aampe's key features

Dedicated per-user agents that learn and act in real time
Automated, continuous experimentation with parallelized testing of thousands of variants
Automatic modeling and generation of actionable insights without manual model building
Causal modeling with per-action causal distributions and counterfactual policy simulation
Enterprise integrations and data ingestion (stream, batch, API connectors) plus real-time content resolution and delivery infrastructure

Aampe use cases

Orchestrate personalized omnichannel campaigns (email, push, SMS, in‑app) using Aampe's agentic real‑time personalization and CDP integrations, automatically running multivariate tests to select winning creative, timing and next‑best actions while providing explainable, experiment‑backed reasoning for each user decision
Automate continuous product recommendation and dynamic pricing experiments with Aampe's continuous feature engineering and causal policy simulation to run counterfactuals at scale, predict revenue lift, and safely roll out optimized models across customer segments
Design and iterate lifecycle programs (onboarding, re‑engagement, churn prevention) using Aampe's automated multivariate testing and agent‑based personalization to tailor interventions per user, continuously measure causal impact, and deploy the best performing policies across channels