Machine Experience, make anything you publish readable by machines
Videos, podcasts, PDFs, images, web pages: make anything you publish readable by machines.
Structure for machines. Explicit for everyone. X-ray your website.
Content Ops is the discipline of creating, managing, improving, publishing, distributing, archiving, and retiring content across every digital channel. Machine Experience (MX) is the layer that keeps that work usable when an AI agent, or any other system, encounters the file outside the environment that produced it.
MX makes digital assets and documents readable by every machine that consumes them, so no machine has to guess. The machine universe is expanding. AI agents, robots, autonomous vehicles, industrial systems, IoT devices, medical instruments, and classes of machine not yet invented all read the same documents.
Your website is a fraction of your content estate. Contracts, policy documents, product specifications, and technical reports never reach the web, but AI agents are reading them anyway, inferring what they can and guessing the rest. Being on the web and being machine-readable are not the same thing.
MX is the DNA a file carries when it leaves any system. It governs what survives extraction, so the next reader can answer the same questions the originator used to answer.
MX builds on what you already have. Schema.org and JSON-LD describe what entities mean, WCAG defines accessibility, Open Graph handles sharing. MX adds governance, provenance, lifecycle state, and agent affordances where those standards leave gaps, never duplicating what they already cover.
A provenance layer for machines
The web and the wider world of data files lack a provenance layer for machines. MX/REGINALD is built to be it.
The industry is preparing to use AI safely inside its walls. Nobody is preparing to be read well outside them. That is the gap, and four parts close it.
- MXis the contract.
- The Gatheringsets the standards.
- REGINALDdoes the signing.
- CogNovaMXoperates the service.
The EU AI Act is the first regulatory forcing function. Other jurisdictions are following.
Schema.org shows the gap in miniature. Structured data tells a machine what something is, but not whether to believe it; Google's deprecation of FAQ rich results is what happens when that gap gets gamed.
The agentic web is the technical and economic case. Machine adoption is exponential, today's web is hostile to it, and cogs replace expensive inference with cheap execution. That is the engine behind the framework, and it is being built now.
Consultancy
MX audit
What an MX audit delivers, and how to verify it. Routes engineers, clients, and auditors to the read that fits them, plus the tools that let any reader verify the deliverable on their own machine.
Implementation
Hands-on engagement after the audit. Gap analysis turns into a prioritised plan, then into shipped patterns across the content estate.
Mentoring
Ongoing advisory for teams implementing MX. Architecture guidance and hands-on support.
Examples
MX in practice. Real-world implementations across different industries and platforms.
New: Why an MX audit pays for itself. Three ways the work returns its cost: reduced inference cost across every reader, fewer hallucinated citations, and lower regulatory exposure under the EAA.
Verify it yourself
Every MX deliverable is built so any reader can verify the evidence chain on their own machine. Drop a PDF into the inspector to read the embedded provenance and conformance signals. Read the explainer to see what each signal means.
MX PDF inspector
Drop any PDF into the inspector to read the embedded MX metadata, the AI provenance sidecar payload, and the PDF/UA conformance declaration. Runs in your browser; nothing uploads.
What MX Compatible means
The explainer walks through the five signals an MX Compatible PDF carries: tagged structure, MX-namespaced XMP fields, conformance declaration, provenance sidecar pair, and embedded AI evidence chain.
Blog
Thoughts on MX, AI agents, and the semantic web.
The Gathering
Community review for Machine Experience. Open drafts, ratified standards, and the round-trip that turns a proposal into canon.
About
Digital Domain Technologies Ltd, trading as CogNovaMX, and Tom Cranstoun.
MX Printworks
Publishing for the AI age.
AI Usage Declaration
Tom Cranstoun's signed statement on how AI was used in writing the MX book series.
What is a COG?
How to read any structured briefing object. A short reference for machines and humans encountering the format for the first time.
Industry News
The dated record of agent commerce launches, platform moves, standards work, and regulatory shifts, newest first.
AI Usage Declaration: read on the web or download the tagged PDF (ISO 14289-1 Level 2).
Compliance and trust
MX makes content machine-readable. REGINALD makes it machine-trustworthy. Together they meet the provenance and accessibility requirements emerging in the EU AI Act, the European Accessibility Act, and digital-records legislation across multiple jurisdictions.
Note: This section describes regulatory frameworks in general terms only. Nothing here is legal advice. Requirements vary by jurisdiction, organisation type, and use case. Consult qualified legal specialists for guidance specific to your situation.
REGINALD
The registry that makes MX-attested content verifiable. Cryptographic provenance, narrowly scoped: this is what the publisher published, unaltered.
Why the agentic era needs infrastructure
Public-sector and regulated-industry content estates need an infrastructure layer, not just intelligence. MX, COGS, and The Gathering provide it.
Ready to make your content estate work for AI agents? Get in touch or email info@cognovamx.com