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Welcome to Selfwin
Understand what Selfwin tracks, what it calculates, and how its approval-gated AI employee turns evidence into work.
Selfwin tracks how brands appear in answers from AI systems. It collects answers for questions your customers are likely to ask, measures which brands and sources appear, and keeps the original evidence behind every number.
Selfwin also has an AI employee. It reviews the tracking evidence, identifies lost prompts, drafts work that could improve them, and puts that work in an approval queue. It does not publish external work automatically.
The product in three layers
Track
Selfwin runs active prompts against the model channels enabled for a project. A completed run can store:
- The prompt, country, model channel, and underlying model identifier.
- The complete answer text.
- Brand mentions, occurrence counts, position, sentiment, and supporting snippets.
- Retrieved URLs, domains, page titles, citations, and source classifications.
- Search fanouts observed while the answer was produced.
The answer is the base unit of the system. Dashboard metrics are summaries of those stored answers, not separate estimates.
Understand
Selfwin turns the collected evidence into four brand metrics: visibility, share of voice, sentiment, and position. It also measures how often sources are retrieved and cited, compares brands across models and topics, and runs separate perception studies against editable market attributes.
Read Metrics overview for the exact formulas.
Act
The Visibility Agent looks for prompts where your brand is absent, competitors lead, another site gets the credit, or your own source is used without your brand being named. It creates evidence-backed actions and drafts for review.
Nothing leaves Selfwin until a person approves it. Approval changes the workflow state; it does not silently publish to a website, community, or third party.
What Selfwin can collect
Provider-backed collection supports OpenAI, Anthropic, Gemini, and Perplexity when the corresponding project credentials are configured. Browser collection is a separate option for supported consumer-facing model experiences and can capture observable search activity that an API response may not expose.
Availability depends on the credentials and browser infrastructure configured for the project. The Settings page is the source of truth for what can run.
Start here
- Follow the Quickstart to create a project and run the first prompts.
- Learn how tracking works before comparing numbers across tools.
- Use the Overview to understand the result.
- Open the Visibility Agent when you are ready to turn a gap into an action.
Documentation and machine access
Every documentation page has an equivalent Markdown URL ending in .md. The documentation index is available at /llms.txt, and the complete corpus is available at /llms-full.txt.
These docs describe implemented behavior. They intentionally exclude planned or speculative features.