Infinite AudienceML

Production-grade machine learning models trained on the Infinite Audience Graph Feature Store. We deploy client-specific models for native, in-graph inference.

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Training Features

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Scorable Identities

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Possible Models

End-to-End Pipeline

From Raw Data to Activation in One Pipeline

In a single automated workflow, your first-party data enters the pipeline, gets resolved against 240M+ identities, enriched with 525+ features, scored by custom ML models, and deployed to activation channels.

ML Pipeline Architecture — from data ingestion through identity resolution, feature enrichment, model training, scoring, and activation

Model Library

Primary Models

Built for every stage of the customer lifecycle, these models help you discover highly qualified prospects while maximizing the value of the customers you already have.

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Lookalike Models

Identify net-new prospects who statistically mirror your highest-value customers. Our pipeline uses your seed audience to uncover hidden graph attributes, then scores the rest of our universe based on those patterns. The result: precision-targeted expansion audiences ranked by similarity.

Seed audience profiling
Percentile-ranked similarity scoring (0–100)
Configurable reach vs. precision thresholds
Automatic feature importance reporting

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Segmentation Models

Automatically discover natural clusters within your data using unsupervised machine learning. Our segmentation engine applies K-means clustering across the graph's feature space to reveal audience archetypes, like high-income suburban families, health-conscious urban renters, and tech-forward brand loyalists. Each segment is defined by statistically significant attribute combinations.

Unsupervised cluster discovery
Dimensional reduction for interpretable cluster profiles
Automated cluster narrative generation using generative AI
Dynamic re-segmentation as graph data refreshes

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Propensity Models

Predict the likelihood of any consumer action, from purchases and subscriptions to churn and campaign responses, using supervised machine learning trained on your historical outcomes. Connect your event data and our AutoML pipeline handles the rest. It automatically selects features, tunes hyperparameters, validates with holdout testing, and deploys a production-grade scoring model directly within the full graph.

Binary and multi-class classification
Automatic feature selection from graph attributes
Holdout validation with AUC/precision/recall reporting
Decile-based performance lift charts

Graph-Native Scores

Your Model Scores Live Inside the Graph

Every model score produced by Infinite Audience ML is persisted as a first-class attribute on each person node in the Infinite Audience Graph. As a result, your propensity scores, lookalike ranks, and segment assignments are instantly queryable, just like any other graph attribute.

WHY THIS MATTERS

Combine scores with any of our graph attributes in a single query
Apply propensity filters on top of demographic and behavioral segments
Re-score automatically as the graph refreshes with new data
Serve scores in real time via API for programmatic bid optimization
Build composite audiences: "lookalike > 0.8 AND income > $100K AND health-conscious"

Graph-hosted model scores visualization — propensity, lookalike, and segment scores attached as attributes on person nodes in the identity graph

Self-Optimizing Architecture

Continuous Performance Retraining Loop

As your campaigns run, the platform continuously ingests new conversion outcomes and feeds real-time performance metrics directly back into the ML pipeline. By actively monitoring for model drift, the engine triggers automated retraining cycles the moment data shifts. As a result, your models constantly evolve and self-optimize, driving progressively higher campaign ROAS without requiring a single manual update.

Retraining TriggerAutomated (on significant drift)

Feedback IntervalContinuous / Real-time

Performance Loop

Accuracy Improvement

Accuracy+4% LIFT

Inference Modes

Batch or Real-Time. Same Model. Same Graph.

Score millions of records in minutes, or a single prospect in milliseconds. Both modes rely on the exact same trained model and validated features from the graph to completely eliminate offline-online skew.

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Batch Inference

Score the entire graph or any filtered segment in a single batch operation. Ideal for campaign audience builds, quarterly re-segmentation, and full-graph propensity refreshes. Results are persisted directly as graph attributes and available for immediate activation.

LatencyMinutes

VolumeMillions of records

Use CaseFull-graph scoring, campaign builds

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Real-Time Inference

Score individual records on-demand via the REST API or MCP. Perfect for real-time bid decisioning, dynamic personalization, and event-triggered scoring. Each request resolves the person against the graph, enriches with current features, and returns a model prediction.

LatencySub-second

VolumeIndividual records

Use CaseAPI scoring, bid optimization

Governance & Operations

Enterprise-Grade Model Registry & Version Control

Deploy with confidence using native model cataloging, dynamic version tracking, and active performance monitoring.

Centralized Registry

A single secure repository to catalog, document, and manage every model in your organization. Track training datasets, performance metadata, and evaluation metrics in one unified dashboard.

Metadata Cataloging

Metric Tracking

Training Lineage

Model Documentation

Versioning & Rollouts

Track version history, compare production candidates against baseline models, and perform zero-downtime promotions or rollbacks without breaking downstream activation channels.

Zero-Downtime Rollouts

A/B Challenger Testing

Instant Rollbacks

Automated Promotion

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Every Model Starts with the Feature Store

The Infinite Audience Graph Feature Store provides validated variables for ML training.

Explore the Feature Store →