Aampe for Data Science
Agentically generate clean, labeled data from daily engagement
With Aampe, non-technical teams deploy communications that build structured datasets as they work, converting everyday messaging into a continuous source of reliable training data for data science. It's a human-in-the-loop engine for data generation.
Overview
Adaptive feature flagging that learns user preferences across models
Aampe replaces binary feature flags with agents that experiment continuously across tagged variants—even competing models—discovering which data-generating process best matches each user's preferences and evolving as those preferences change.
Benefits
Agentically manage user-level experimentation and treatment optimization
Clear causal signals for deeper explainability and smarter exploration
Agents estimate causal impact for every individual interaction, not just aggregate outcomes. This lets data scientists separate correlation from causation, trace why specific interventions worked, and explore which behaviors most strongly drive change — enabling attribution in a cleaner, de-noised data environment.
Message
Value Proposition
Offering Category
Call to Action
Continuous feature engineering through semantic abstraction
Teams define abstract concepts, and Aampe agents automatically populate those dimensions with user-specific data. The result is a living, high-resolution feature space that updates in real time for downstream models.
Causal distributions for every action, enabling counterfactual policy simulation
Every user interaction feeds into a Beta distribution representing the causal strength of that action. These distributions allow teams to simulate new policies, compare hypothetical interventions, and quantify uncertainty — turning experimentation data into a flexible substrate for policy design and evaluation.
Product
Collaboration without coordination overhead
Aampe's agentic infrastructure orchestrates experiments intelligently, so marketing and product teams can launch new ideas without disrupting active tests.
Data scientists can deploy updated models directly into the same interface, where agents route predictions into clean, abstracted features that other teams can use immediately.
Integrations
Integrate seamlessly & deploy in days
Al-driven optimization works with your existing tech stack—no SDKs, custom development, or complex setup needed. Sitting on top of current communication platforms, it enables rapid deployment without heavy engineering work, so teams can focus on performance, not implementation.
Data Sources
Customer Data Platforms
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Data Warehouse & Storage
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CONTENT MANAGEMENT SYSTEMS
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Recommendation Engines
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Agentic Actions, Content & Data
Delivery Infrastructure
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Engagement Tools
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Observability & Insights
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In-App Tools & Native Surfaces
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FAQ