kn0w reads every audited company against its cohort — companies of the same sector, jurisdiction, and size, under the same regulator. The dataset is anonymised at a five-company floor and held by the issuer of the audits, an institution with no advisory, platform, or billable-hours line to protect. It is an external record of where a company’s AI governance stands, held to one evidence standard across the cohort.
A reading of where a company sits on AI governance requires a peer dataset built to one evidence standard across every company in the cohort. The constraint is custody. A holder that also sells into the same market carries a conflict against the data it holds: a consultancy would hold advisory it could sell against it; a vendor would be a party whose own controls are measured within it; a law firm would bill hours against it. kn0w carries none of those lines of business, which is the condition under which the dataset is held.
The dataset is the second of the two artefacts every audit produces. The first is the signed Statement delivered to the member — the member’s own named result. The second is the member’s contribution to the dataset, anonymised, which the next member is read against. The Statement is named and belongs to the member; the contribution is anonymised and belongs to the cohort. They are distinct artefacts and never the same document.
5
k = 5
The two artefacts resolve on different clocks. The Statement is issued on commission of the audit: the member’s named result, measured against the framework their regulator names them accountable for, standing on its own terms whether or not the peer placement has resolved yet. The placement — the member’s reading against the cohort — is the second layer, and it holds to a floor.
Every contribution is made under a single Data Contribution Agreement naming KN0W PTE. LTD. as data controller. No public scraping. No regulatory-filing inference. No simulated peers. The dataset exists because members commissioned audits and consented to their anonymised contribution joining it.
The dataset is cut on three axes: sector, jurisdiction, and staff band. A member is read against the cohort they fall into, not against an average of the whole.
A member is read against the cohort they fall into, not against an average of the whole — under the framework their regulator names them accountable for.
Each cohort operates under the framework its members answer to. An Australian FinTech under APRA and ASIC is not read against a UK HealthTech under the MHRA — the regulatory context, operational shape, and governance obligations differ, and the reading would not be defensible across them. Each cohort is a distinct peer set.
Funding stage is captured at intake but is not a cohort axis. It is self-reported, blurs at the edges, and does not hold across funding events. Sector, jurisdiction, and staff band define the peer set.
Six dimensions sit behind this placement, each read against the cohort’s distribution. The full dimensional reading is delivered in the Statement itself.
Illustrative placement from kn0w’s published sample Statement.
Issuance
Read against the current dataset
+ Q1
New audits added at k=5
+ Q2 · Q3
Cohort keeps growing
Anniversary
Re-read against the current dataset
Each new audit contributes to the cohort it sits in, at the k=5 floor, from the quarter it is issued.
A member’s reading is computed against the dataset as it stood at issuance. At the institutional anniversary, the Annual Statement re-reads the member against the dataset as it then stands — issued under the same hallmark and identifier structure as the founding Statement. The dataset a member is benchmarked against is the dataset on the day the artefact issued, not a figure that moves under them between issuances.
The construct behind the reading — the six dimensions, their weighting, the k-anonymity floor, the bias controls, and the regulatory frameworks mapped into the instrument — is documented in full at /methodology.
Data handling and residency are documented at /security. The dataset is governed under a single Data Contribution Agreement naming KN0W PTE. LTD. (Singapore) as data controller.
The Statement records where a company’s AI governance stands against its true peers — under the framework its own regulator names it accountable for, not an industry average, not a synthetic composite, not a vendor-held figure. It is the answer to the question a board, its investors, and its regulator are already asking.