Use Case
AI Training
Train AI Models on Real Human Behavior
Give your models a view of how people actually make decisions.
As AI moves to the center of discovery, recommendation, and automation, the quality of training data has become a competitive advantage in its own right.
Most models today rely on scraped web data, synthetic training loops, or controlled interaction environments. These sources do not reflect how people actually navigate apps, compare options, switch between platforms, or make decisions in real-world contexts.
Public and synthetic sources reveal little about how users really discover products, compare alternatives, respond to incentives, or navigate a decision across several apps.
The result is a widening gap between how models are trained and how people actually choose.
Are Your AI Systems Trained on the Highest-Quality Behavioral Data?
RealityMine provides continuous, real-world behavioral insights that capture how people actually navigate the digital ecosystem — across apps, platforms, and journeys.
Benefits
What You Can Do With RealityMine
RealityMine captures real, session-level activity across apps, platforms, and consumer journeys. By observing how people discover, evaluate, and engage in natural environments, we can generate proprietary behavioral signals that reflect real decision-making rather than simulated interaction.
Subject to consent, compliance, and feasibility, these signals can be structured for integration into AI and machine-learning pipelines to support training, fine-tuning, evaluation, and ongoing improvement.
With RealityMine, You Can:
Train on observed sequences of discovery, comparison, and purchase behavior
Improve recommendation and ranking systems using real-world behavioral patterns
Ground AI agents in how people evaluate options and make decisions across platforms
Reduce dependence on synthetic loops that can amplify bias and model drift
Evaluate model performance against changing real-world behavior over time
Build on proprietary behavioral signals that are not available to competitors training on the same public data
The next generation of AI will not be defined by model architecture alone — it will be defined by the quality of the signal used to train it.
