Documentation
Learn how Signal measures and improves AI visibility
Practical guides for setting up a workspace, reading the numbers correctly, and turning observations into improvements.
New to Signal? Start here
Set up a workspace and read your first baseline in about ten minutes.
Getting started
Measuring visibility
Choosing and organizing prompts
How to build a prompt set that reflects real buyer questions, and how topics and intent labels shape your reporting.
Answer engines and data sources
Which engines Signal observes, how live observations are collected, and how sample data is labeled and kept separate.
Metrics, denominators, and confidence intervals
Definitions for every Signal metric, how each is calculated, and why every rate is shown with its sample size and a 95% interval.
Citations and source analysis
How Signal collects the sources engines cite, and how to use source data to find the pages and third-party sites that shape answers.
Improving visibility
Accuracy monitoring and the fact sheet
Record the facts that matter about your organization, review claims engines make, and turn inaccuracies into corrections.
AI readiness checks explained
What each readiness check tests, which AI crawlers Signal evaluates in robots.txt, and how scores are calculated.
Tasks, evidence, and reports
How Signal creates evidence-linked tasks from observations and audits, and how to share period reports with stakeholders.
Account and billing
Team members and roles
Invite teammates, assign roles, and understand what owners, admins, editors, and viewers can do.
Billing, trials, and plans
How the 14-day trial works, what each plan includes, and how to change or cancel a subscription.
Data handling and security
What data Signal stores, how accounts are protected, how to export or delete data, and which providers process it.