frog

Frog: GEO for API and developer-tool teams

Frog helps marketing and growth teams at API, developer-tool, and developer-infrastructure companies investigate how their products appear in AI answers and prepare evidence-backed content improvements.

From a buyer question to a verifiable page change

Start with a specific customer need or buying question, then record the product facts and sources that should support the answer. Run a live check and inspect the saved answer and citations before deciding what to change. If the evidence shows a useful content gap, prepare a focused Markdown change in the authorized file and review the proposed diff in a pull request. Your team can then verify the content and merge it through its normal deployment process. This workflow makes the proposed change reviewable; it does not establish that a mention is a recommendation, that a citation converts, or that the content change will produce an AI recommendation.

Who is Frog for?

Frog is designed for small marketing teams selling technical products with public documentation. Their buyers compare use cases, integrations, deployment options, pricing and alternatives before choosing a tool.

What can I do in early access?

Create a project, record product facts, add buyer questions, and run live AI checks. Inspect the saved answers and citations before choosing an improvement. The agent can help prepare content drafts and, for an authorized GitHub Markdown file, a reviewable pull request.

External changes depend on the integrations and permissions configured for your project. A draft or pull request is not a published page. The complete autonomous workflow is still being developed.

How does the GitHub workflow work?

Connect an authorized repository and choose the supported content file. Review the proposed content and destination before approving a pull request. Your team reviews and merges the change through its normal deployment process. Frog does not automatically merge a pull request as part of this workflow.

What do live checks measure?

Live checks currently use the OpenAI Responses API with web search. They record API answers and cited sources, not conversations in the ChatGPT application. Different questions, models, locations and dates can produce different results.

Compare equivalent observations over time. A mention is not necessarily a recommendation, a citation is not a conversion, and a content change does not guarantee that an AI system will recommend your product.

What should I prepare?

  • Your product website and public documentation.
  • The customer needs and buying questions you want to investigate.
  • Verified product facts and the sources supporting them.
  • A GitHub Markdown file your team is authorized to update, if you want to test the pull-request workflow.

Read the planned pricing or join early access.