AI Procurement Pulse Kinetic Gain Research · Dated issues
STANDING METHODOLOGY · DATED RESULTS

Dated research on vendor AI governance disclosure.

The AI Procurement Pulse publishes dated readings of public /.well-known/ disclosures against applicable Kinetic Gain Protocol Suite documents. Issues #1–#4 and a methodology baseline are available. The series targets a quarterly cadence; this page is not a live scan.

Read the May 2026 baseline for the engine v0.4 discriminator and its reported signature checks on eleven Kinetic Gain documents. Those are historical results: a valid signature does not establish schema conformance or current publication status. Issue #5 has not been published on this site.
See also Issue #4 — The Scaled Mirror, Issue #3 — Three-Day Drift Soak, Issue #2 — The First Non-Zero Row, and Issue #1 — The Zero Baseline. This page remains the standing methodology.

Read the Baseline → Request issue updates
12
Open Suite specs · endpoint coverage varies
4
Dated public issues linked
May 2026
Historical v0.4 baseline
$0
Public-edition price

Why the world needs a procurement Pulse

A school district vetting an AI tutor today reads a 40-page vendor whitepaper, a HIPAA attestation, and a marketing PDF. A hospital vetting a clinical-decision-support tool does the same. A federal agency does the same. Every buyer reinvents the same review wheel because vendors don't disclose in a comparable format.

The Suite makes disclosures machine-readable. The published Pulse issues show what their dated crawls observed, including gaps in publication. Read each issue's universe, method, and limits before comparing vendors.

For buyers

Review dated aggregate findings and the underlying method before asking a vendor for its own current evidence.

For vendors

See what the dated crawls found and check whether your own public disclosures remain valid and current.

For researchers

Use the published methodology and issue data to study where public self-disclosure is present or absent.

Preview — the chart formats each issue uses

These charts are projected, illustrative mocks. The published issues replace them with real crawl data from the vendor-domain universe.

Disclosure publication rate by vertical

PROJECTED · ILLUSTRATIVE
FinTech
62%
EdTech
45%
HealthTech
38%
Federal contractors
29%
CPG / Retail
12%

% of in-scope vendor domains in each vertical publishing any Suite document at /.well-known/. Higher = more transparent procurement posture.

Signed-vs-unsigned ratio

PROJECTED
23%
ed25519-signed
  • Signed via hash-attestation-rs · 23%
  • Unsigned · 77%

Drift events per 1k checks

PROJECTED
Q3 '26Q4 '26Q1 '27Q2 '27Q3 '27

Hypothetical drift rate over four quarters as adoption matures.

The methodology, end-to-end

Published issues link their methods and data artifacts. Reproducing a reading requires the same dated universe, probe rules, and disclosure snapshots; current sites may differ.

  1. Universe selection. Each issue names its dated domain universe and links its available data artifacts. Universe sizes differ between issues.
  2. Crawl. aeo-crawler probes applicable /.well-known/ document paths. The Suite currently has twelve specifications; not every spec maps to a public endpoint.
  3. Validate. Every fetched document goes through aeo-validator-service. Invalid documents are flagged but counted as published for the publication-rate numerator.
  4. Signature check. If a document references a signing_key_url, hash-attestation-rs verifies the ed25519 signature against the canonical-JSON hash.
  5. Drift detection. Dated snapshots can be compared for changed content hashes and version fields; see each issue's published drift artifact.
  6. Reporting. Published issues report dated aggregate findings, including publication, validity, signature, and drift measures where available. The linked public reports do not publish per-vendor rows.

Operational crawl settings belong to each dated run. The linked crawler source describes the implementation; a present-day run and its rate limits have not been verified by this page.

The same six steps above, mapped to the real tools behind each one and grouped by phase. Click a step to filter, click a fact for detail. Tab through with a keyboard; Enter/Space activates a node like a click.

Methodology detail

Jump to methodology ↓

Questions the published issues explore

§ 1 · The Public Index

Dated aggregate publication rates using each issue's documented domain universe.

§ 2 · The Vertical Deep-Dives

Selected vertical cuts, with their own sample sizes and disclosure limitations.

§ 3 · The Drift Report

Which probed documents changed between available dated snapshots.

§ 5 · The Signature Honor Roll

Signature results where the issue could verify them, reported separately from document validity.

§ 6 · The Methodology Appendix

Each issue's published method and available data links, including what could not be measured.

DATED ISSUES · FREE PUBLIC EDITION

Request updates about Issue #5.

The button opens an email draft to Kinetic Gain. Send the draft to request updates; this site does not automatically register a subscription.

Or just bookmark this page. Or email pulse@kineticgain.com and we'll add you manually. Issues #1–#4 and the May 2026 baseline are published; Issue #5 has not been published here.

What this is, in one paragraph

The AI Procurement Pulse is a research publication about AI vendor governance disclosure using the open Kinetic Gain Protocol Suite as its substrate. It exists because AI procurement is the largest under-measured market in software, the disclosure data can be publicly fetched and compared at a dated point in time. Issues #1–#4 and the May 2026 baseline are published; Issue #5 is not yet available here.

The Pulse is part of a wider governance and procurement ecosystem. Buyers can move from this research layer into the protocol docs, validation surfaces, and decision-card examples without leaving the Kinetic Gain network.

Docs hub

docs.kineticgain.com provides quickstarts, implementation guidance, and the broader Kinetic Gain operator stack context.

Buyer-side tools — evaluate one vendor depth-first

The published Pulse issues report dated aggregate readings. To review a specific vendor's current disclosure, start with the separate apex tools:

Vendor AI Disclosure Review

/trust/vendor-ai-disclosure-review/ — 10-dimension buyer-side rubric. Found × quality × notes per dimension. Verdict bands (well-disclosed · standard · sparse · red-flag). Auto strengths/gaps/follow-ups. The per-vendor counterpart to Pulse's population measurement.

AI System Card Builder

/trust/ai-system-card/ — the inverse: document your own AI systems for the customers and auditors who'll ask. 14-field form, markdown + JSON export.

The Trust Pack (8 tools)

/trust/ on the apex — Evidence Locker · Shadow AI Discovery · AI Vendor Intake · Tabletop Kit · Risk Register · Subprocessor Disclosure Template · plus the two above. Browser-only, no login, no telemetry.