Control sampling methods, filtering thresholds, and feature engineering approaches. Tune how raw data is transformed into the signals that power your product.

Optimize the foundation of your insights.

Change visualization styles, density of information, or interactivity levels. Experiment with how users see and interact with data to find what resonates best.

Validate user experience and engagement.

Tune retrieval depth, prompt styles, memory persistence, or response strictness. Test the trade-offs between creativity and precision in your AI agents.

Optimize for user trust and task completion.

Knob: Diversity vs. Relevance.
Tune the recommendation engine to favor either highly relevant items to increase conversion, or diverse items to increase discovery. The business can validate which balance drives more long-term revenue.

Knob: Anomaly Confidence Threshold.
A higher threshold reduces false positives but risks missing subtle issues. A lower threshold flags more, requiring more human review. Hospitals can validate workflow efficiency and patient safety trade-offs.

Knob: Retrieval Grounding Strictness.
More grounded means fewer hallucinations but slower responses. More open is faster and more conversational. Teams can test which balance users prefer — speed vs. strict reliability.