AI Visibility
Monitor how AI engines respond to the queries that matter for your clients — capturing mentions, recommendations, and citations across a prompt universe built around each business.
What AI visibility monitoring actually is
AI visibility is the measurable presence of a business in the answers AI engines return to real-world queries. When someone asks ChatGPT, Claude, or Perplexity which HVAC company to call, which law firm to trust, or which restaurant to try — visibility is whether your client appears in that answer, and in what context. GeoScript measures this systematically by running a purpose-built prompt universe against multiple engines on a repeating schedule, preserving each raw response as an immutable observation.
The prompt universe
For each client, GeoScript generates approximately 1,100 prompts across five categories — service discovery, comparison, local, reputation, and category-level queries. These are deterministic: the same prompt set runs each monitoring cycle so results are comparable over time. Every prompt is submitted as a real API call to the engine's production endpoint. GeoScript preserves the raw HTTP response body before parsing, so the observation record reflects exactly what the engine returned.
Which engines are monitored
ChatGPT (OpenAI), Claude (Anthropic), and Perplexity are live today — real API observations, production adapters, smoke-tested with real API round-trips. Gemini, Google AI Overview, and Microsoft Copilot integrations are in development; no production observations exist for those engines yet. GeoScript publishes the exact status of every engine integration rather than implying uniform coverage.
How this connects to the next step
AI visibility observations are the foundation everything else is built on. Opportunities are surfaced because GeoScript can detect visibility gaps in the market before a client is signed. Once a client is active, monitoring continues: the same observation engine that fed the pitch now produces the longitudinal data that drives recommendations, deployment decisions, and outcome measurement. Every part of the loop depends on the integrity of these observations.
What AI Visibility is not
Explicit limitations (§127)
- ✕GeoScript does not have access to any engine's internal ranking signals, training data, or model parameters.
- ✕GeoScript cannot observe dwell time, click-through behavior, or how users interact with AI responses after they are returned.
- ✕GeoScript observations are API-level calls — not user-session replays. Individual users may receive different, personalized responses.
- ✕Observation results reflect a point in time. GeoScript cannot guarantee a future response will match.
- ✕GeoScript does not monitor search engine rankings, paid advertising, or social media visibility.
Related
Other pillars
Find businesses with measurable AI visibility gaps, score each opportunity, track your pipeline, and deliver evidence-backed pitches — before a competitor does.
GeoScript crawls a client's public website, extracts a structured content evidence base, and builds the Content Genome that powers every recommendation.
Understand which AI crawlers and agents are reaching a client's site, what they're doing, and why — with verified identity and declared purpose.
Turn Content Genome findings into specific, typed recommendations — then deploy them through an approval-gated, verifiable system that leaves an immutable record.
Immutable, period-scoped reports that capture AI visibility change, deployments made, and measured outcomes — shareable with clients and presentable under agency branding.
Every observation, recommendation, and outcome lives in a structured, tenant-isolated, immutable-where-it-matters data architecture — the evidence base the entire platform reasons over.