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 Content Ops work usable when an AI agent, or any other system, encounters the file outside the environment that produced it.
We don't sell generic MX packages. We partner with you to implement what works for your specific context.
Every team has different needs, constraints, and opportunities. Our approach adapts to your situation.
Four phases
Phase 1, Understand
We start by deeply understanding your current state:
- How do agents currently interact with your site?
- Where are they succeeding? Failing?
- What is your competitive landscape?
- What are your business priorities?
- What constraints exist (technical, team, timeline)?
Deliverable: MX Readiness Assessment with ranked recommendations.
Phase 2, Plan
We collaboratively design your path forward:
- Define measurable success criteria
- Sequence implementation by impact and dependencies
- Identify resource requirements
- Plan for knowledge transfer
- Establish testing and validation approach
Deliverable: Strategic MX Roadmap with clear milestones.
Phase 3, Implement
We work hands-on with your team:
- Add Schema.org markup to priority pages
- Fix accessibility issues blocking agents
- Make implicit states explicit
- Validate with agents and tools
- Document patterns for replication
Deliverable: MX-compliant code ready for production.
Phase 4, Enable
We ensure you can maintain and expand:
- Train your team on MX principles
- Create checklists and decision frameworks
- Document patterns specific to your stack
- Establish ongoing testing procedures
- Provide post-launch support
Deliverable: Self-sufficient team that understands MX.
Phase definitions
- Phase 1, Understand
- A diagnostic phase in which we examine how agents currently interact with your site, where they succeed and fail, your competitive landscape, your business priorities, and the technical, team, and timeline constraints that shape what is possible. Output: an MX Readiness Assessment with ranked recommendations.
- Phase 2, Plan
- A collaborative design phase in which we define measurable success criteria, sequence implementation by impact and dependencies, identify resource requirements, plan knowledge transfer, and establish the testing and validation approach. Output: a strategic MX roadmap with clear milestones.
- Phase 3, Implement
- A hands-on build phase in which we work with your team to add Schema.org markup to priority pages, fix accessibility issues blocking agents, make implicit states explicit, validate changes with real agents and tools, and document patterns for replication. Output: MX-compliant code ready for production.
- Phase 4, Enable
- A knowledge-transfer phase in which we train your team on MX principles, create checklists and decision frameworks, document patterns specific to your stack, establish ongoing testing procedures, and provide post-launch support. Output: a self-sufficient team that understands MX.
- Continuous, Measure
- Across every phase we track the success metrics defined in Plan, agent traffic, accessibility scores, SEO impact, and business outcomes, and report on them regularly so the value of MX is visible to stakeholders.
Phase Summary
| Phase | Purpose | Deliverable |
|---|---|---|
| Understand | Diagnose current state and constraints | MX Readiness Assessment |
| Plan | Design the path forward | Strategic MX roadmap |
| Implement | Build MX-compliant code | Production-ready implementation |
| Enable | Transfer knowledge and set up sustainment | Self-sufficient internal team |
What makes us different
1. We practise what we preach
This website is an example of Machine Experience. Browse it with an AI agent: everything is structured, accessible, and explicit.
2. We focus on impact
You do not need to fix everything. We help you identify the 20% of changes that deliver 80% of value.
3. We transfer knowledge
We do not create dependency. We make your team self-sufficient through training, documentation, and frameworks.
4. We measure results
We define success metrics upfront and track them throughout: agent traffic, SEO impact, accessibility scores, and the ROI behind them.
5. We stay current
AI agent capabilities evolve rapidly. We track developments so our recommendations reflect the current state of practice.
Engagement principles
No generic solutions. Every recommendation is specific to your context.
Impact over perfection. Quick wins first, full implementation second.
Knowledge transfer. Your team should understand MX as well as we do.
Measurable outcomes. We track and report on defined success metrics.
Honest assessment. Sometimes the answer is "not yet" or "focus elsewhere first".