Welcome to CiteLadder
Understand where your brand appears, find the evidence behind it, and turn what you learn into better work.
CiteLadder brings AI search visibility, website health and connected search data into one project. Use it to answer three questions: Where are we showing up? What should we improve? Did the change appear in later measurements?
These guides take you from your first project to an evidence-backed improvement. If you already have data, go straight to Actions or working with the Agent.
Find your starting point
| What you want to do | Where to go |
|---|---|
| Set up a brand and collect a first baseline | Your first project |
| See how AI answers describe or cite your brand | AI Visibility |
| Find technical and content issues on your website | Site Health |
| Understand demand and traffic from search | Performance and integrations |
| Turn a finding into a plan, page edit or draft | Meet the Agent |
| Use saved CiteLadder data in another AI assistant | Connect with MCP |
How the pieces fit together
- Collect a baseline. Review the questions you track, run an audit or crawl, and connect the data sources relevant to your project.
- Understand the evidence. Open the actual answer, page or search record behind a summary. Check when it was observed and what was covered.
- Choose an Action. Review the proposed work, its target and the findings that support it.
- Create and review. Work with the Agent to produce something your team can use. You approve the work and make the external change.
- Measure again. Declare implementation, then review what later compatible observations show.
You do not need every integration to get started. Begin with the question you need to answer; add another source when it helps you make a decision.
Dashboard, Agent or MCP?
Dashboard is where you inspect your project’s measurements and underlying records. Agent is where you work through a question or Action and refine a saved deliverable. MCP lets an external AI client read the saved records your account can access.
The Agent and MCP both work with saved evidence. Opening a page or asking for a read does not refresh the underlying website, run an audit or buy a dataset.
A useful habit from day one
Before sharing a finding, include its project, date window, engine or data source, and any coverage limitation. “Our brand appeared in this audit’s successful answers” is a more useful conclusion than “AI always recommends us.”
Start with your first project, or keep the evidence guide nearby while reviewing results.