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
Independently evaluated
Rigr AI took part in the Australian Age Assurance Technology Trial (2025), an independent evaluation commissioned by the Australian Government and conducted by the Age Check Certification Scheme, tested against a dataset the trial built rather than a vendor’s own.
The trial’s assessment found Rigr AI strong on technical readiness, privacy alignment and usability, with reliable threshold-based age decisions, fast on-device processing and low false-positive rates in real-world scenarios — and noted the configurable buffer zones that let a relying party manage uncertainty instead of relying on one hard threshold, which is the same wedge described above.
For a Trust & Safety team, the relevant part is not a leaderboard position but that the behaviour was checked by someone other than us, on data we did not choose. The individual test report is public.
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.
Frequently asked questions
What is the difference between age estimation and age verification?
Age estimation infers an apparent age from an image. Age verification checks an identity document or an authoritative record to establish a stated age. Rigr provides estimation: a calibrated apparent-age signal for routing content to the right review queue, not a proof of identity.
What does apparent age mean?
It is how old a face appears, not a claim about the person's true age. The distinction matters most for synthetic and AI-generated characters, where there is no ground-truth age to verify at all.
Why does the uncertainty value matter for review routing?
Because two identical point estimates can need different handling. A face estimated at 18.2 with high uncertainty needs a human; the same estimate with low uncertainty may not. Rigr returns both numbers so that difference is visible in the workflow rather than buried in documentation.
Can it handle AI-generated or synthetic imagery?
Yes — this is a large part of why it exists. AI-generated and manipulated imagery creates age-ambiguous review queues that are expensive to staff and easy to get wrong in either direction, and apparent age is the only meaningful signal where no true age exists.
Can we benchmark it against our own data first?
Yes. Bring a representative sample and your current policy thresholds, and the private benchmark pilot returns a calibration view, a false-positive/false-negative trade-off analysis, review-band recommendations and an integration estimate.
Does it make the moderation decision?
No. Rigr informs review; it does not make policy or legal determinations. Borderline and low-confidence cases are designed to escalate to trained human reviewers.
Has the age estimation been independently tested?
Yes. Rigr AI took part in the Australian Age Assurance Technology Trial (2025), commissioned by the Australian Government and conducted by the Age Check Certification Scheme, which tested participants against a dataset the trial built rather than a vendor's own benchmark. Its assessment found Rigr strong on technical readiness, privacy alignment and usability, with low false-positive rates in real-world scenarios. The individual test report is public.