How It Works
From scan to fix to re-scan: a repeatable workflow for improving AI visibility
Gemmetric helps teams measure how AI systems currently understand the business, identify what is limiting confidence, and turn that into changes they can actually ship.
The workflow is simple: establish the current state, inspect the underlying signals, produce fix-ready outputs, and validate the next scan after changes go live.
The goal is not theory. It is a clearer operational loop with evidence at every stage.
The workflow
Five steps that turn AI visibility into an operating workflow
The point of the workflow is to make AI visibility measurable and actionable. Each step produces a clearer picture of what AI systems can understand, where confidence breaks, and what your team should do next.
1) Establish the current business definition
Start with how the business should be defined: what it is, what it does, how it should be categorized, and which facts are supposed to be canonical.
2) Inspect what AI systems can actually retrieve
Review the on-site and off-site signals AI systems are likely using so the team can see where meaning is clear, incomplete, or inconsistent.
3) Measure the score and the reasons underneath it
Turn the current state into score breakdowns, evidence, and diagnostics so the team knows what is driving the result instead of just seeing a number.
4) Produce fix-ready outputs
Translate the findings into schema, metadata, copy, FAQ, and structural recommendations your team can actually implement.
5) Re-scan and validate what changed
After fixes are published, run the next scan so the team can compare deltas, confirm what improved, and decide what to prioritize next.
The questions we answer
Gemmetric is built around the same evaluation loop AI assistants run behind the scenes.
- Can I crawl this site? (GEO prerequisite)
- Do public sources consistently back up what this business claims? (GEM)
- How do models currently define and understand this business? (AI Perception)
- Has this business published a usable canonical identity? (AI Identity)
- Should I surface this business in an answer, or hedge? (AI Visibility outcome)
What moves through the workflow
What the team learns, ships, and validates at each stage
The handoff needs to stay clear. The first scan creates a baseline. Implementation turns findings into work. The re-scan shows whether the underlying picture actually improved.
After the first scan
The team sees the current state, the strongest constraints, and which issues are suppressing confidence most.
During implementation
Findings turn into concrete work across structure, corroboration, content clarity, and identity publication.
After the re-scan
The team compares deltas, verifies what actually improved, and decides whether the next pass should go deeper or shift focus.
After fixes
What happens after fixes are applied
The workflow does not end at recommendations. The goal is to validate what changed and carry the next scan forward with evidence.
1) Publish fixes
Apply the recommended schema, metadata, copy, and structural changes.
2) Re-scan the site
Run another scan so the same signals can be checked again under the updated site state.
3) Compare deltas
Review what improved, what stayed flat, and which constraints still limit AI Visibility.
4) Prioritize the next pass
Use the new evidence to decide which fixes should be deployed next.
The bottom line
When an AI is asked about your business, how confident is it, really? Gemmetric gives you the honest answer, the evidence, and the fixes to improve it.