A professional network for AI researchers and practitioners, structured around a review obligation rather than passive connections. The network only exists because everyone in it does real reviewing - not just networking.

It is AI-sector specific, purpose-built for how fast AI research actually moves - not general academia. And it is practitioner-inclusive: business owners and operators with genuine experience qualify, not only credentialed academics.

AI is collapsing how long research takes to produce. What it hasn’t sped up is how research gets checked. In fast-moving fields, work can be out of date before it is ever validated - and the slow part was never the thinking, it was the reviewing.

That is the mismatch this network is built for. If the work can be done in weeks, the credibility around it has to be earned in weeks too, without dropping the rigour that made peer review worth trusting in the first place.

This is a nationally scoped network by design, not merely as a starting point. It stays the Australian AI research peer network as it grows - not a global platform that happens to have started here. Scope is part of the credibility, not a limitation to grow out of.

Membership isn’t gated by a single credential like a published book. It’s scored across a weighted matrix, so a candidate can qualify by stacking evidence across paths rather than needing one specific qualification. That’s what lets an experienced operator and a published academic qualify on equal footing.

See how membership is assessed →

Every member has a profile built around research credibility rather than job history: a bio, domain-expertise tags, certified papers, and ORCID, book or portfolio links. Connections are built only through the invite mechanic - you can’t simply add anyone - which keeps the network’s credibility intact.

Everyone a member invites becomes a visible, attributed part of their network, and each member’s profile shows who brought them in. The whole platform forms a traceable trust chain rather than a flat directory. A member’s certified papers and reviewer standing live here - which is the reason to stay active beyond the review obligation alone.

The closest precedents each solve part of this. OpenReview brings structured, AI-specific review, but is conference-bound and academic. Peer Community In and Review Commons offer journal-independent, portable review - the closest match to our certificate model. ResearchHub runs member-driven review, but on a crypto token - a model to learn from, not copy.

No existing platform combines AI-sector focus, Australia-specific scope, practitioner inclusion, networked profiles and portable certification. That combination is the gap.

The deeper gap is that no one has yet built a way to give fast, practitioner-led work the credibility peer review confers - validation that keeps pace with the research instead of lagging years behind it. That is what this network exists to close.