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GeoScript is launching the AI search visibility category — monitoring what every major AI engine says about a business, and automatically optimizing it. We're happy to support journalists and researchers covering this space.
Quick facts
- Company name
- GeoScript, Inc.
- Founded
- 2026
- Headquarters
- Austin, TX
- Stage
- Pre-launch
- Markets
- US, Canada, UK, Australia
- Category
- AI search visibility & optimization
- Press contact
- press@geoscript.ai
Company descriptions
GeoScript monitors what every major AI engine says about a business and automatically optimizes it — the first platform to fix AI search invisibility, not just report on it.
GeoScript is an AI search visibility and optimization platform for agencies and local businesses. A single JavaScript snippet installs in under two minutes and continuously monitors citations across ChatGPT, Claude, Gemini, Perplexity, Grok, and Google AI — then automatically deploys schema optimization, AI crawler tuning, and structured content improvements to improve those citations over time. Every optimization is informed by a pre-launch benchmark dataset of 105 million queries across 250 industries and 4,000+ cities across the US, Canada, the UK, and Australia. GeoScript is built in Austin, TX.
Founder
Building GeoScript from Austin, TX. Previously built Gradelog, a SaaS platform for the construction industry. Focused on AI-native tools for the local businesses and agencies that need them most.
alex@geoscript.aiAvailable for interviews, background briefings, and data previews. Reach out at press@geoscript.ai.
Brand assets
Logos, colors, and product screenshots for press use. Full press kit available on request — email press@geoscript.ai.
Brand colors
Data angles available to journalists
GeoScript is building the largest structured AI citation dataset in existence — 105M+ queries across 250 industries, 3,500 cities, and 6 AI engines. The research below will be available for press use under embargo or with attribution. Reach out to discuss early access.
Market structure
Who does AI actually recommend — and does it match Google?
AI engines and traditional search surface meaningfully different results. The businesses that rank on Google are often not the ones AI recommends, and vice versa. We're building the first dataset to measure this divergence at local market scale — by industry and city across the US, Canada, the UK, and Australia.
Do ChatGPT, Gemini, and Perplexity agree on who to recommend?
Different AI engines are trained differently, retrieve differently, and cite differently. For any given industry and market, the businesses each engine recommends may have almost no overlap. We're building the first cross-engine comparison at this scale — across 250 industries.
Which industries and cities are most invisible to AI search?
AI citation rates vary dramatically by industry and geography. Some categories have high citation density. Others are almost entirely absent from AI answers. We'll have the first structured dataset to map this across 250 industries and 4,000+ cities in four countries.
How does AI citation behavior differ between the US, Canada, the UK, and Australia?
AI engines trained primarily on English-language content may behave very differently across English-speaking markets. Citation rates, preferred sources, and which business attributes predict citation may vary significantly by country. No research has measured this at local market scale.
Is there a 'citation gap' between large cities and small markets?
A business in a major metro may face more AI citation competition than one in a smaller market — or the opposite. The relationship between market size and AI recommendation density is completely unmeasured. We'll have the data to answer it at every population level.
What drives citation
What do cited businesses actually have in common?
Schema types, review counts, content structure, freshness, page accessibility — which of these actually predict whether AI recommends a business? We're building a correlation model across all cited businesses in our dataset. The findings will be the first empirically grounded answer to this question at scale.
Does schema markup actually help AI citation — or is that a myth?
Schema is widely recommended for AI visibility. But does it actually drive citation, and for which engines? The answer may be more nuanced than conventional wisdom suggests — and it likely differs significantly between Google AI and non-Google engines like ChatGPT, Claude, and Perplexity.
Do review count and rating predict AI citation — and which platforms matter?
AI engines train on web content including review platforms. Does Google review count predict Google AI citation? Do Yelp reviews predict Perplexity citation? The relationship between review signals and AI recommendation — by engine, by industry — is entirely unmeasured at scale.
How much of a typical business website is invisible to AI crawlers?
AI bots read websites differently from Googlebot and differently from human visitors. JavaScript-rendered content, navigation bloat, and content structure all affect what AI actually reads. First-party data from inside real websites is the only way to measure this — and no one has done it at scale.
Does content freshness affect AI citation? And by how much, per engine?
Recency appears to be a signal for AI citation — but the weight likely varies by engine and category. ChatGPT, Perplexity, and Gemini may weight freshness very differently. We'll be able to measure this correlation across industries and engines at a scale no previous study has approached.
Which third-party sources does each AI engine trust — by industry?
AI engines cite from a mix of business websites, directories, review platforms, and news sources. Which third-party sources carry the most citation weight for each industry and engine is almost entirely opaque. Our dataset will map the citation source graph by category.
Business impact
The AI referral attribution gap: how much traffic from AI is invisible in analytics?
When AI engines cite a business, the resulting website visit often shows up as direct traffic in GA4 — or not at all. The gap between actual AI-driven traffic and what standard analytics reports is likely significant and largely invisible to most businesses. This is measurable with first-party script data at scale.
Do visitors from AI citations actually convert? How does it compare to other channels?
Early signals suggest AI-referred visitors may convert at higher rates than organic search visitors. If confirmed at scale and across industries, this fundamentally changes how businesses should think about AI search — not just a brand visibility metric, but a customer acquisition channel.
What does AI invisibility actually cost a local business?
If AI engines are now involved in a meaningful fraction of local purchasing decisions — contractor hires, legal consultations, healthcare choices — businesses not appearing in AI answers are leaving real revenue on the table. Quantifying that cost, by industry, is a story nobody has told with data.
The optimization gap: $189M+ invested in AI visibility dashboards, zero in fixing the problem
The funded competitors in AI search visibility — Otterly, Peec, Profound, AthenaHQ — all monitor and report. None deploy a single optimization to a client's site. Agencies doing this work manually bill $2,500–8,000/month per client. The category has built dashboards. It hasn't built solutions.
Methodology
How you ask AI matters as much as what you ask — prompt intent and citation behavior
The same business may appear in AI answers to some phrasings of a question and not others. Transactional queries ('who should I hire for X') likely trigger different citation behavior than informational queries ('what is X'). Mapping prompt intent to citation outcome — across industries and engines — is a novel research problem with practical implications for every local business.
Do AI engines agree more in some industries than others?
Citation consensus — whether ChatGPT, Gemini, and Perplexity all recommend the same business — may be much higher in some categories than others. High consensus may indicate a strong quality signal exists; low consensus may indicate the category is still unstructured in AI training data. Measuring this is new.
The GeoScript AI Visibility Index — quarterly research publication
A recurring benchmark: citation rates by industry, by city, by engine — across the US, Canada, the UK, and Australia. Which industries are most visible to AI? Which markets are underserved? What changed quarter over quarter? Available to press ahead of public release.
Interested in any of these? Email press@geoscript.ai with your publication, timeline, and what you're working on.
Get in touch
Press inquiries, data requests, embargo arrangements, interview requests, and asset downloads. We respond within one business day.