We build measurement for AI search.
GetRecited helps marketing teams inspect AI answers about their brands, compare competitors and decide which content to work on next.
- What do the engines say?Sampled answers, kept verbatim.
- Why do they say it?The sources each answer cited.
- How do we change it?Grounded drafts and brand files.
What makes AI answers hard to track
Answers vary
A recommendation can change between engines and between runs. Teams need to see more than one response.
Context matters
A mention can be a recommendation, a comparison or a caveat. The full answer explains the difference.
Sources are visible
Cited pages give teams somewhere to start when they investigate how a brand is described.
How we build GetRecited
Measure before you claim
Repeat the questions, read the responses, and account for variation before interpreting a change. A small sample cannot settle every question.
Keep the evidence
Keep collected responses with their prompts, engines and citations so the team can check a result in context.
Ground what you generate
Prepare drafts from reviewed brand facts. Check their claims and sources, and require approval before publication.
Make brands readable by machines
Publish approved information as llms.txt and JSON-LD, or share it with connected agents through Brand MCP. Engines still choose what to read and cite.
What we are building next
- Automatic crawler-log connectors for the main CDNs and hosts, so agent analytics needs no manual upload.
- Publishing connectors for common CMSs, so an approved draft can go live without leaving GetRecited.
- Grok, Copilot Search in Bing, Copilot in Edge, Google AI Mode and Meta AI coverage, subject to collector availability and validation.
Early-access members see the roadmap in detail and vote on what ships first.
Talk to the team.
Questions about the product, a pilot, or a partnership. We reply within one business day.