Six surfaces, measured differently, declared honestly.
Every engine below is included in every plan. They are not, however, the same kind of measurement — two are reached through a search vendor, four through grounded model APIs, and each has a failure mode that changes how its figure should be read. Here is all of it, including which adapters are sourcing answers today and which are still in build.
What is measuring today.
Two different facts get confused with each other on pages like this, so they are separated here. Bundled is commercial: every engine is in every tier, permanently, with no per-engine add-on. Live is operational: which adapters are sourcing answers right now. The first does not move. The second does, and it moves in one direction.
This table is generated from the same constant the monitoring loop reads to decide which engines it can query. There is no second list to keep in step, so the page cannot quietly stay flattering after the product changes — which is the only version of a status page worth publishing.
A cycle running against fewer engines is not a broken cycle. It reports the legs it reached, the bands widen to match the smaller sample, and the coverage line on the report names what is missing — the same machinery that handles a vendor outage handles an adapter that has not shipped yet. See how six legs become one number.
The per-engine add-on is a margin trap.
It is the category’s standard pricing structure: two or three models in the headline price, the rest sold separately — roughly €20–30 per engine at one competitor, and $7 to $115 a month per engine depending on tier at another.
For a single brand it is a line item. For an agency running twenty clients across six engines it is a compounding cost that arrives after the client contract is signed, and it makes “which engines do you cover?” a question with a commercial answer rather than a technical one.
So all six are bundled, and the unit economics were solved first — batch submission and a mid-tier analysis model cut cost per run by 79% with the mention rate unchanged. A bundle whose costs were never modelled is a bundle that gets quietly degraded in month eight.
How the economics workNot included, on purpose
- Microsoft Copilot. Minority coverage across the field, add-on-only where it is offered at all, and no demand signal from this market. Available if a market asks.
- Consumer-interface scraping. Higher fidelity, terms-of-service exposure your client would inherit, and a cost base that does not survive channel pricing. A calibration tier is on the roadmap; a scraping-first product is not.
- Engines we cannot geo-target. Not excluded — but where a market cannot be located, the figure says so instead of pretending.
ChatGPT
GPTIn buildHow we reach it
Grounded model API with web search enabled
Location control: User-location parameter on the request
Its specific failure mode
Search does not always fire. In published testing on identical prompts, roughly 58% of API runs never performed a web search at all — so an absence can mean the engine never looked rather than looked and found nothing.
Every capture records whether search fired. Runs that did not search are visible and can be excluded, rather than silently depressing a mention rate.
Claude
CLDLiveHow we reach it
Grounded model API with the web-search tool
Location control: Country, city and timezone on the search tool
Its specific failure mode
Location control is the most complete of the assistants, and there has been an open report of incorrectly localised results — so it is validated rather than assumed.
Our South African smoke tests compare located against unlocated runs on local-intent prompts, and the comparison is repeated when the adapter changes.
Gemini
GEMIn buildHow we reach it
Grounded model API via Vertex
Location control: Coordinates through Vertex only — the consumer developer API exposes none
Its specific failure mode
The straightforward developer API has no location control whatsoever. A tool using it is producing an unlocated figure and, unless it says so, presenting it beside five located ones.
We route through Vertex for geo-grounding and label the method on the capture. Where a market cannot be geo-grounded, the figure carries the limitation rather than hiding it.
Perplexity
PLXIn buildHow we reach it
Grounded model API
Location control: Full location filters
Its specific failure mode
The most citation-dense surface we measure, which makes it the most useful engine for source analysis and the least representative for answer length.
Reported on its own leg as well as in the aggregate, because a citation-heavy engine can dominate an unweighted source table.
Google AI Overviews
AIOIn buildHow we reach it
Third-party SERP vendor
Location control: Vendor geo targeting, plus device
Its specific failure mode
Detection is imperfect. Published benchmarks put best-in-class AI Overview detection near 68% — about a third of genuinely present overviews return nothing. Trigger rate also varies enormously with intent: around 36% on informational queries against 5–8% on transactional ones.
Presence is tri-state — present, absent, not detected. Undetected observations are excluded from rates and counted on the report, so a bad vendor week widens a band instead of inventing a decline.
Google AI Mode
AIMIn buildHow we reach it
Third-party SERP vendor
Location control: Vendor geo targeting, plus device
Its specific failure mode
Live in South Africa since August 2025 and, since March 2026, answering in Afrikaans, isiZulu, Sesotho and Setswana. Costs roughly twice an organic SERP call to retrieve and takes appreciably longer.
Included in every tier despite the cost, because it is the surface most likely to become the default way South Africans search.
How six legs become one number.
The aggregate is a market-weighted roll-up, not a flat average. An engine nobody in the market uses should not carry the same weight as one everybody does.
| Step | What happens |
|---|---|
| Per-engine leg | Each engine's own band and run count, computed only from its own runs |
| Coverage check | Legs that did not complete are excluded and named, rather than being treated as zeroes |
| Market weighting | Legs are weighted by the market's engine mix — local usage, not global averages |
| Aggregate band | A binomial interval over all contributing runs, reported with total n and coverage |
A six-engine aggregate and a four-engine aggregate are different measurements, and the product never presents one as the other. When coverage drops, the band widens and the report says which legs are missing.
See it against your own client list
A working demo runs your prompts, in your market, on live engines — not a sandbox with seeded data. Bring one client brand and three competitors.