Product-grade apparent-age estimation for Trust & Safety teams.
Rigr helps platforms route age-ambiguous AI-generated and user-uploaded imagery with point estimates, calibrated uncertainty and configurable review bands — reducing noisy escalation while improving control over high-risk content workflows.
The problem with a single label
Trust & Safety teams do not just need a label such as “adult” or “minor.” They need to know how uncertain the system is, how to route borderline content, how to tune false positives and false negatives, and how to defend the threshold used for human review.
AI-generated and manipulated imagery has made this harder: age-ambiguous content — both synthetic and user-uploaded — creates review queues that are expensive to staff and easy to get wrong in either direction. Generic prompting against a general-purpose vision-language model is not a calibrated, defensible safety control.
The wedge: a point estimate is not enough
A face estimated at 18.2 with high uncertainty is operationally different from a face estimated at 18.2 with low uncertainty — the first needs a human, the second may not. Rigr surfaces both numbers, not just the label, so that difference is visible in the workflow, not buried in technical documentation.
| Example result | Interpretation | Suggested workflow |
|---|---|---|
| 15.4 ± 0.8 | High-confidence minor-presenting signal | High-risk queue / immediate specialist review |
| 17.8 ± 2.5 | Borderline and uncertain | Escalate to trained human review; do not rely on a binary threshold |
| 22.1 ± 0.9 | Low-risk apparent-adult signal | Lower priority or sample audit, depending on policy |
| 19.0 ± 5.0 | Low confidence despite an adult point estimate | Manual review or secondary model signal |
What Rigr returns
- Apparent age, not a claim of true age — especially relevant for synthetic or AI-generated characters, where there is no ground-truth age to verify
- Calibrated uncertainty alongside the point estimate, for routing and escalation decisions
- Face-level metadata, so teams can route high-risk and borderline content to the right review queue rather than a single undifferentiated pool
- Human-in-the-loop escalation for borderline and low-confidence cases — Rigr informs review, it does not make policy or legal determinations
Private benchmark pilot
For teams currently relying on generic VLM prompts, off-the-shelf classifiers, or manual-only queues, Rigr offers a private benchmark pilot: bring a representative sample and your current policy thresholds, and we return a calibration view, a false-positive/false-negative trade-off analysis, review-band recommendations, and an integration estimate.
See Age Estimation for the underlying model and API, and Deployment, sovereignty & information control for private and on-premise deployment options.