Can an agent find, buy, and recommend your content?
Model the full machine journey. See where it breaks, why it breaks, and which metadata or policy change removes the friction. Results are scenario estimates, never claims of live indexing.
Content profile
Choose a baseline, then make it yours.
Machine journey needs work
Complete the profile to calculate where an autonomous agent is likely to encounter friction.
Follow the agent, decision by decision.
Run the simulated journey one checkpoint at a time. Each step names the signal used, the current decision, and the most useful recovery action when the path fails.
Discovery
The agent starts from a public listing, search index, or known catalog.
- Signal inspected
- Catalog presence
- Current result
- Not run
- Recovery
- Analyze profile
- Confidence
- —
Prove what changed.
Save a baseline, improve the weak signals, then compare the score and the exact machine-readable capabilities gained.
Before
Saved baseline
Current
Live profile
Make the next agent decision easy to explain.
Export a compact readiness report with the assumptions, scores, generated metadata, and prioritized fixes.