Comparison
Scraped answers are not GEO data
Four ways to get generative-engine data. Getting the answer is the easy part, and every option does it. They differ in what happens to that answer before it reaches your database.
Feature by feature
What you get, what you still build
"Build it" means the capability is missing and the work lands on your side, per engine, for as long as you run it.
| Capability | API for GEO | AI scraping API | GEO dashboard tool | In-house build |
|---|---|---|---|---|
| Raw answer text | Yes | Yes | Sometimes | Build it |
| Citations with domain + category | Yes | URLs only | In the UI | Build it |
| Entity resolution across aliases | Yes | No | Yes, closed | Build it |
| Average position in answer | Yes | No | Yes, closed | Build it |
| Share of voice | Yes | No | Yes, closed | Build it |
| Sentiment per brand per response | Yes | No | Partly | LLM cost + eval |
| Theme clustering | Yes | No | Rare | Research project |
| Per-brand perception inside a theme | Yes | No | Rare | Research project |
| Shopping products, merchants, prices | Yes | Raw block | Rare | Build it |
| Fan-out follow-up questions | Yes | No | Rare | Build it |
| Social / video citation layer | Yes | No | Rare | Build it |
| Model & locale coverage as reference data | Yes | Varies | Fixed list | Your problem |
| Programmatic access to everything | Yes | Yes | Export only | Yes |
| Resale / white-label | Allowed | Allowed | Usually not | Yours |
| Ongoing maintenance when engines change | Ours | Partly theirs | Theirs | Yours |
The gap, concretely
Same prompt, two payloads
A scraping API returns
answer only{
"query": "best project management tool
for creative agencies",
"model": "chatgpt",
"answer": "For creative agencies, Loopwork
is often recommended for its proofing
workflow, while Cadence is favoured by
larger studios... Northbeam is a
lighter-weight option...",
"links": [
"https://review-site.com/pm-tools",
"https://loopwork.io/agencies",
"https://forum.example.com/thread/812"
]
}
// then you build:
// alias matching, position parsing,
// share of voice, sentiment scoring,
// theme clustering, shopping parsing,
// per-model quirks, re-runs, storage API for GEO returns
answer + computed layer{
"answer": "For creative agencies...",
"entities": [
{ "name": "Loopwork", "type": "brand",
"position": 1, "mentions": 3,
"shareOfVoice": 24.1,
"sentiment": { "score": 76,
"label": "positive" } },
{ "name": "Cadence", "type": "competitor",
"position": 2, "shareOfVoice": 19.6 }
],
"citations": [
{ "url": "https://review-site.com/pm-tools",
"domain": "review-site.com",
"category": "review",
"mentionsBrand": true }
],
"themes": [
{ "theme": "proofing workflow",
"perception": { "Loopwork": "leader",
"Cadence": "adequate" } },
{ "theme": "pricing", "share": 18.2 }
],
"shopping": [],
"fanOuts": [
"which pm tools have client approval",
"how much does pm software cost per seat"
]
} Cost of the alternative
What in-house actually costs
Six engines, six shapes
Each engine formats answers, citations and shopping blocks differently, and changes them without notice.
Entity resolution
Brand names collide, abbreviate and inflect. Without alias merging your share of voice is quietly wrong.
Scoring at scale
Sentiment and theme extraction on every response is an LLM bill, plus the evaluation work that keeps the scores stable.
Locale reality
Model availability differs by country. Getting a French answer that a French user would see is infrastructure work.
Glossary
GEO vocabulary, defined once
Generative Engine Optimization (GEO)
Making a brand appear, rank and be favourably described inside AI-generated answers. Measured per prompt and per model, where SEO measures per keyword and per position.
AI visibility
The share of in-scope AI responses that mention a given entity at least once, from 0 to 100. Endpoint.
Share of voice
An entity's mentions divided by all entity mentions in scope, a relative measure, unlike visibility.
Average position
The mean rank of an entity in the answers where it appears; lower is better.
Citation rate
The share of responses that cite at least one source attributed to the entity.
True reach
Of the pages a model cited, the share that also mention the brand: the link between the answer layer and the source layer.
Query fan-out
The sub-questions an engine derives from a prompt in order to retrieve and compose its answer. Endpoint.
Perception
How a brand is characterised inside a specific theme (leader, cheap option, hard to use), which a single global sentiment score cannot show.
Credit
One prompt, run once, on one model, in one locale, with every enrichment layer included.
FAQ
Choosing between them, answered
What is the difference between an AI scraping API and a GEO API?
A scraping API returns the raw generative answer. A GEO API returns the answer plus the computed layer: resolved entities, share of voice, average position, sentiment, themes, per-brand perception, shopping offers and fan-out questions.
Should we build GEO tracking in-house?
The scraping is the easy part. The recurring cost is entity resolution across aliases, per-model answer parsing, sentiment and theme extraction, shopping parsing and keeping up with interface changes on six engines.