An independent method, written down.
We build the layer that tells you what is on a channel before you buy it: independent, measured at the channel level, and open to review.
Not built alone.
The method is developed with researchers, universities and industry experts, and written to be rigorous enough to survive outside review. Partners and published work will be named here as the collaborations formalise.
- AcademicWorking with university researchers on the methods behind channel classification and measurement.
- IndustrySenior measurement and agency experts pressure testing the approach against how plans are actually run.
- Open reviewMethodology written to be inspected, not taken on trust. Detail published as it is validated.
Keeping ads off content made for kids.
Advertising on or beside children's content is a legal, brand-safety and ethical risk, and the signal most buyers rely on, a creator's own "made for kids" tag, misses too much. Architect stacks four independent layers of evidence so no single gap lets kids content through, takes the call down to the individual video, and keeps a person in the loop for the uncertain cases.
Platform labelling
YouTube's own made for kids label, honoured first. Formally labelled content is excluded automatically.
Video level check
Rolls up the individual videos on a channel, catching unlabelled channels with a high share of children's content.
Our own children's model
The catch net for the undeclared: scans titles, metadata and channel signals, and scores each channel.
Manual control
Ongoing manual quality assurance and spot checks on what the model decides.
Regulation is moving the same way. The EU Digital Services Act and the updated US COPPA rule restrict behavioural, profiling-based advertising to minors, not advertising by content. The compliant path is contextual: exclusion decided at the level of content, with no person-level data. That is what this model does. The aim is to extend it beyond YouTube, and to validate it in the open with authorities and the research community, as a transparent model we intend to open source.
Built on the industry's frameworks, graded by our own method.
The safety composite draws on published industry standards and third party verification signals as inputs. The final grade is ours. Formal certifications will be listed here as they are granted, with date and scope.
GARM
Brand safety and suitability framework. Industry taxonomy for the composite score.
Verification
Third party verification signals ingested as inputs, never as the final grade.
Platform signals
Strikes, demonetisation, monetisation tier.
Historical flags
Architect's own incident graph.
Manual review
Analyst override with a written rationale.
Channel aggregate, never individual.
Our architectural principle under GDPR. Every score is built from channel level signals. No person level data is collected, processed or stored. Any audience data integration is gated behind a data protection impact assessment before it ships.
- ReproducibilityEvery figure sourced, timestamped and re-runnable.
- IndependenceRatings are produced outside the buy and outside the platforms, from a method that is written down.
- CertificationsNone claimed yet. Updated as third party audits and certifications are completed.
Principles we hold to.
Independent
Ratings are produced outside the buy and outside the platforms, from a method that is written down rather than assumed.
Channel aggregate
Every signal is measured at the channel level. No person level data is collected, processed or stored.
Reproducible
The intent is that every figure is sourced, timestamped and re-runnable. We would rather show the working than the headline.
Open to review
The method is written to be inspected and challenged, with detail published as it is validated.