How do you build an AI reputation monitoring dashboard?

An AI reputation dashboard is not a marketing dashboard with AI metrics bolted on; it is a different category of tool, because the underlying data is different. What makes it work is consistent, multi-engine polling underneath a small set of decision-grade dimensions.

The dimensions that belong on the dashboard

Sentiment

Scored by engine and aggregated, so you can see both the overall picture and where individual engines diverge.

Source quality

Scored by how authoritative the sources the engines cite actually are.

Theme distribution

Which framings the engines are applying to the brand.

Peer comparison

How the brand reads against the relevant, named brand set running the same prompts.

Share of voice

Prominence at the category level – how much of the conversation the brand owns.

Trend lines

All of the above tracked over time, so the dashboard shows how the picture is moving, not just where it stands today.

The dashboard is only as good as the data underneath it

These dimensions require data that polls the major AI engines on a consistent, repeated basis using the same prompts each time – which is what AIQ was built to provide, tracking the eight AI engines AIQ currently monitors (ChatGPT, Gemini, Copilot, Perplexity, Claude, Grok, Google AI Overviews, and Google AI Mode). Building a dashboard without that data infrastructure – manual screenshots, one-off audits, or partial-coverage tools – produces something that looks like a dashboard but cannot actually support decisions across the engines.