Core answers about methodology, coverage, and how teams use AI visibility intelligence.
We measure how AI systems describe, recommend, and compare your brand in each target market, then turn that into signals like recommendation presence, rank placement, sentiment direction, citation quality, and factual consistency.
AI Share of Voice is the percentage of relevant prompts where your brand is mentioned or recommended versus competitors. It helps you understand market exposure in AI channels, not just web search traffic.
SEO tools track rankings and traffic on traditional search engines. We focus on generated answers from AI assistants and answer engines, where users often receive recommendations without clicking through to websites.
You create a project, define market context, generate prompts, and run executions across supported model families. The outputs are normalized into visibility trends, rank tracking, and reputation signals that the team can act on.
Audit Deep Dive is the section-by-section expansion of the latest brand audit. It stores evidence, implications, recommended moves, monitoring prompts, and suggested tasks so the audit becomes an operating layer, not just a snapshot.
Core signals include sentiment distribution, narrative risk, citation dynamics, source patterns, recommendation share, and consistency of brand framing over time.
Coverage includes leading model families and answer engines. Specific providers may evolve over time, but your dashboard always reflects the active coverage and methodology in your workspace settings.
Yes. You can configure language and country context at project level to track regional differences in perception, recommendation patterns, and sentiment instead of reading one global average.
Smart execution is designed to optimize and maximize the value of your execution limits, prioritizing the prompts and timing that deliver the most signal for the budget available. In practice, the right cadence depends on how quickly your category changes.
Fan-out queries are the follow-up searches an AI system makes behind the scenes before it answers. You can think of them as the model breaking one prompt into smaller research tasks, like checking comparisons, proof points, reviews, pricing, or alternatives. Monitoring them helps you understand which questions the model still needs answered before it feels confident recommending a brand.
Each successful model request consumes one execution credit. Paid plans reset credits to the plan allowance at each billing renewal, while free plan credits are fixed and do not auto-renew.
Execution Status controls whether a project or prompt is eligible to run. Only active projects and active prompts execute. Paused items stay saved but are skipped until reactivated.
If your new plan has lower limits, the system automatically pauses the newest projects first. If needed, it then pauses prompts one by one from the newest remaining project until usage fits your plan.
We compare generated claims with your verified brand context and configured project information. Potential inaccuracies are flagged so teams can prioritize corrections by business impact instead of chasing every minor variation.
You cannot directly edit third-party model training data. You can, however, improve future outputs by strengthening source quality, clarity, and consistency across your public content and trusted references. The platform shows where to focus first so the work stays practical.
Yes. Teams can review metrics in the dashboard and share recurring summaries for marketing, brand, communications, and leadership stakeholders.
Yes. Agencies and in-house groups can manage multiple brands/projects, compare performance, and standardize reporting across accounts.
Most accounts are led by Growth, Brand, SEO/Organic, or Product Marketing teams, with regular visibility into leadership, communications, and revenue teams.