OneGlanse

What is OneGlanse?

OneGlanse is an open-source GEO and AI visibility tracker designed to show how a brand appears inside multiple AI providers. It measures presence rates, mentions, sentiment, and rankings across provider experiences that include ChatGPT, Gemini, Perplexity, Claude, and Google AI Overview.

The core purpose is to help teams evaluate and monitor brand visibility in AI-generated responses by running reproducible prompt testing and aggregating results into an analytics view. It is positioned as self-hostable, using users’ own accounts and infrastructure.

Key Features

• Self-hostable, free and open-source agents to track brand appearance on your own infrastructure.
• Provider coverage across multiple LLM experiences including ChatGPT, Gemini, Perplexity, Claude, and Google AI Overview.
• Reproducible prompt testing to review how your brand is mentioned when prompts are run in a consistent way.
• Presence rate, mentions, sentiment, and rank metrics (including an “Avg rank across prompts” view) to quantify visibility.
• Visibility scoreboard and comparison views across competitors, including metrics like visibility, mentions, and sentiment.
• “Review provider-rendered answers” UI that shows rendered outputs “exactly as users see them,” along with analysis metrics.

How to Use OneGlanse

• Deploy and configure OneGlanse on your own infrastructure and connect it to your own provider accounts (as described by the site).
• Define or import the prompt set you want to test for brand visibility across the supported AI providers.
• Run prompt tests and collect results so the platform can compute visibility, mentions, sentiment, and ranking metrics.
• Use the visibility scoreboard to compare your brand against competitors and review the provider-rendered answers in the unified view for context.

Use Cases

• Brand visibility monitoring across LLM providers: track whether and how often a brand is mentioned, how sentiment is scored, and where it ranks within prompts.
• Competitor benchmarking: compare visibility and related metrics across competitor brands (e.g., visibility %, mentions, and sentiment) using the scoreboard view.
• Debugging prompt performance: run the same prompts repeatedly to see how brand positioning changes over time, and use rendered-answer review to understand why results differ.
• GEO-aware assessment: evaluate how responses may vary by geography (the product is described as a “GEO tracker”) to identify where brand presence appears strongest or weakest.
• Review and validation workflow for AI outputs: inspect provider-rendered answers as users experience them, using the included analysis metrics to guide decisions.

FAQ

• Is OneGlanse open source and self-hostable? Yes. The site describes OneGlanse as free and open source, and as self-hostable using your own accounts and infrastructure.

• Which AI providers does OneGlanse track? The page lists ChatGPT, Gemini, Perplexity, Claude, and Google AI Overview.

• What kinds of metrics does it report? The site mentions presence rate, prompts mentioning your brand, average rank across prompts, visibility, mentions, and sentiment, plus a position/ranking indicator.

• Does it show the actual AI answers, not just summary metrics? Yes. It includes a “Review provider-rendered answers” view intended to show answers exactly as users see them, alongside analysis metrics.

• Does the platform support reproducible testing? The page states that it supports reproducible prompt testing to track brand visibility.

Alternatives

• Self-hosted AI monitoring/observability tooling: If you want similar multi-provider measurement but not specifically brand visibility and GEO tracking, consider general observability approaches that log and analyze AI outputs.
• AI answer evaluation and prompt testing frameworks: For teams focused on testing prompt-to-output behavior rather than brand visibility scoreboards, prompt evaluation tools can be used to run repeatable tests and score results.
• Marketing attribution and competitive intelligence analytics platforms: If your primary goal is broader marketing analytics rather than provider-rendered AI answer inspection, analytics platforms can complement AI visibility tracking, though they may not cover AI provider experiences directly.
• Custom data pipelines for LLM response logging: Teams with engineering resources can build pipelines to call providers, store outputs, and compute visibility and sentiment, but that shifts effort from a ready-made tracker to bespoke implementation.