A European Public-Sector GEO Pilot: Mapping the AI Visibility Landscape Before Scaling

How Saltanat Labs used Rankscale to map priority prompts, citation sources, and execution-ready fixes across a complex public-sector website - turning a vague GEO mandate into a concrete backlog.

Partner agency: Saltanat Labs · Author: Lily Grozeva

Priority Prompts

Benchmarked across answer engines

Citation Sources

Mapped per topic and entity

Fast-Track Fixes

Surfaced and prioritised by feasibility

The Client

The client is a European public-sector organisation operating a large informational website that spans multiple internal entities and topic areas. With AI answer engines now intercepting a growing share of citizen and stakeholder queries, the team needed a clear map of how their content and citations are currently positioned inside those generative answers - before committing any resources to broader GEO work.

The Ask

Where does the organisation actually stand inside AI answer engines - and which improvements are realistically shippable? The client wanted a clear map of priority prompts, the citation sources competing for those answers, and a prioritised list of fixes their internal teams could execute inside an existing governance workflow.

Steps Taken in Rankscale

1. Audit & Benchmark
   Saltanat Labs used Rankscale to benchmark current visibility across a controlled set of priority prompts and to map citation sources across the major answer engines. This established a hard baseline of where the client showed up - and where they didn't.

2. Diagnose & Prioritise
   Page-level diagnostics surfaced execution-ready improvements across structure, positioning, and citation alignment. Each opportunity was scored against the client's real-world governance constraints, so only feasible fixes made it into the recommendation set.

3. Sequence & Track
   Recommendations were sequenced into fast-track improvements and a broader rollout path. Rankscale dashboards were set up against the pilot baseline so progress on priority prompts can be tracked as governance blockers clear and fixes ship.

Results

• A full map of priority prompts and citation sources. Rankscale produced a hard baseline of where the client currently shows up across a controlled set of priority prompts and who is being cited instead. For the first time, the team has a single, shared view of the AI visibility landscape they operate in.
• A prioritised backlog of execution-ready fixes. Page-level diagnostics surfaced concrete structural, positioning, and citation-alignment fixes, each scored against the client's governance constraints. A vague "do more GEO" mandate became a sequenced backlog internal teams can actually ship.
• A shared playbook and dashboard for what comes next. Saltanat Labs and the client now share the same language, benchmark, and Rankscale dashboard. As governance blockers are cleared and fixes roll out, progress is tracked against the pilot baseline instead of being debated in meetings.

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