No black box. Every forecast shows its work.
What drives accuracy
- Segment size. Forecasts for a whole region are often more reliable than forecasts for a small neighborhood or a niche customer group. Accuracy should be reported for each segment, never implied to be uniform.
- Type of question. Overall support for an option is often easier to forecast than a precise share in a small group.
How AlphaVu measures it
- Back-testing. We compare predicted with observed outcomes across campaigns, elections and policy decisions.
- Holdout validation. When we train and evaluate each digital twin, we test it against data held back from training. This applies to the twin itself, not to every individual question a user asks.
- Ongoing monitoring. Accuracy metrics are refreshed continuously, so you see how the model performs as conditions change, not just in a one-time demo.
- Self-correction. The twin tests its own forecasts against incoming opinion, fine-tunes when accuracy starts to drift, and tells you when it's time for a new survey. This capability is patent pending.
- Low-confidence warnings. When confidence in a result is too low, AlphaVu warns you or doesn't show the result.
Red flags in any AI opinion tool
- A single headline accuracy figure with no explanation of what was tested.
- No breakdown by segment.
- Forecasts without confidence ranges.
- A model that never says where it's unsure.
See how AlphaVu Predict works for audiences like yours →
Related: Trust Center · A Self-Improving Digital Twin That Checks Its Own Work · What is a synthetic audience?