The depth of the data and how easy it is to get at. We track visibility across the major AI engines, including Google AI Overviews and AI Mode, in several regions and languages. Everything can be cut by topic, engine, competitor and time period, and mentions and citations are reported separately, which makes it much easier to see what is actually moving.
Source analysis is the feature we use most. It shows which publishers and pages AI answers cite for our topics, which has directly shaped where we invest in third-party placements.
The MCP and API access is a big plus. Our team pulls Limy data straight into Claude and our own reports instead of exporting dashboards, which saved a lot of manual work.
The team is also responsive. When we hit data questions, we got clear answers quickly on Slack. Review collected by and hosted on G2.com.
The recommendation impact reporting is the area with the most room to grow. We’d like clearer definitions of impressions and impact, consistent before-and-after windows, and a way to separate a recommendation’s effect from market-wide shifts. Clearer year-over-year success metrics would also help us show the value of the recommendations internally. Review collected by and hosted on G2.com.
