Four tools. Each ends with a concrete recommendation.
Trackers show where you stand in AI answers. These tools show what to do next: which sources are missing, what prompts your customers type, what is asked under your brand.
Citation Gaps. Source audit with recommendation.
Which sources recommend you today, which do not, and which three gaps to close first. Citation Gaps combines visibility data with source analysis and returns a prioritized list with reasoning per lever.
- Compares your source coverage to competitors, across all tracked models.
- Highlights recurring gaps that multiple AI models share.
- Suggests sources most likely to move the needle, with a short reasoning per pick.
- Saves every analysis as history so you can track progress, not one-off snapshots.
Prompt Research. What your customers actually type.
Which prompts lead to which answers today. Prompt Research surfaces the wording that is common in your market and shows how leading AI models currently respond.
Suggests prompts in your topic you would not guess on your own, not generic volume.
Each suggestion carries a 1-to-5 confidence score so you see which prompts the model rates as relevant.
Filter by topic area and push selected prompts directly into your tracking set.
Fanout Queries. What LLMs ask under the hood.
A user types one sentence, and AI models generate multiple sub-queries underneath. Fanout reveals those sub-queries so you see what LLMs actually look up under your brand.
The main prompt.
A user types one sentence, end of analysis. You do not see the sub-queries the LLM fires under the hood, and so you do not see the sources it actually pulls from.
The actual sub-queries.
What LLMs really search for under your brand, with the sources that answer each sub-query. Transparent where providers do not expose their own fanout.
AI Readiness. One score, one actionable lever.
How well is a page prepared to be cited by AI models. AI Readiness returns a score per URL and a list of the three biggest levers, written so they can be executed.
- 01Evaluates structure, citations, topical authority, and technical discoverability.
- 02Surfaces missing schema markup, thin sections, missing evidence.
- 03Recommendations are actionable: no "improve content", but "add an FAQ on topic X".
- 04History per URL so you can see whether your changes worked.
Others show numbers. We show levers.
Most dashboards show a score and leave interpretation to you. Across three brands per week that is a lot of work.
Every recommendation carries the data point and the logic that produced it. No black box.
Twenty possible levers are often as unusable as none. Three ranked levers a week is what teams actually ship.