City tracking vs geo-grids vs coordinate targeting, compared
Three ways to measure local AI visibility compared: what city tracking, geo-grid scans and coordinate targeting each measure, which engines they cover, what drives cost, and when each fits.
Quick verdict
City tracking tells you how a business shows up for people in a city, every day, across many engines. A geo-grid tells you how that changes across a service area on the day you scan. Coordinate targeting tells you what AI says to someone standing at a specific spot, such as outside your storefront, and tracks that spot over time.
None of the three replaces the others. For most multi-location brands and agencies, city tracking is the base layer, coordinate targeting is the upgrade for storefronts that matter most, and a geo-grid is the occasional deep look at one neighbourhood. Promptwatch is the platform we'd run the first two on, because it attaches crawler logs, citations and conversion data to the same monitors.
The three methods at a glance
| City tracking | Geo-grid scans | Coordinate targeting | |
|---|---|---|---|
| Question it answers | Are we named when people in this city ask? | Where in our service area are we named? | What does AI say to someone at this exact spot? |
| Unit of measurement | A prompt panel run from a city | One AI sample per grid point | A prompt panel run from one coordinate with a radius |
| Typical cadence | Daily (Promptwatch) | Per scan; Local Falcon recommends weekly or bi-weekly | Daily (Promptwatch) |
| Example tools in our catalog | Promptwatch (Business and agency plans); location-level modes in Uberall, BrightLocal, Synup, Birdeye | Local Falcon | Promptwatch (Business, Agency Scale, Enterprise) |
| Engines affected | All models on the monitor | ChatGPT, Gemini, Google AI Overviews, Google AI Mode; Grok per Local Falcon's FAQ | ChatGPT, Google AI Overviews, Google AI Mode; other models fall back to country, state or city |
| Main cost driver | Prompts x models x locations against a plan allowance | Grid points x keywords x locations x platforms x frequency | Same as city tracking, on a higher plan |
| Output | Time series per city | A map for each scan | Time series per point |
City tracking
This is the closest thing to how most people picture AI rank tracking, with a place attached. You define prompts, choose models and set the monitor's location. In Promptwatch, monitors carry a country plus an optional state and city, a language and an optional persona, and the prompt panel runs daily per model. Country-level is on every paid plan; state and city start on Business ($579/mo) and are on every agency plan from Kick-off ($199/mo).
Several listings suites measure "per location" too, though our catalog describes them differently. BrightLocal anchors up to 20 prompts to each location. Synup writes 16 prompts per location that name the category and city. Uberall offers location-level tracking from GEO Scale. Birdeye reports visibility by brand and by location. Each of those comes with its own engine list and cadence, ranging from daily runs on Uberall Scale to a monthly default on Birdeye and a fixed monthly refresh on Synup.
City tracking fits when your customers think in towns rather than streets: a regional law firm, a chain with one branch per city, a service business covering a metro area. It's also the cheapest way to compare many locations on the same footing, because one prompt set can be repeated across cities.
Geo-grid scans
A geo-grid borrows the map-pack method from local SEO. In our catalog, Local Falcon is the AI-focused example. It lays a grid of 9 (3x3) to 441 (21x21) points around a location, sends the same keyword to an AI platform from every point, and reports Share of AI Voice: the share of points where AI mentions you. Its knowledge base suggests an 11x11 grid to start, with a radius of about 2 miles in cities, 5 in suburbs and 10 in rural areas.
The strength is spatial detail you can show a client on a map. The cost is the meter. Local Falcon charges one credit per grid point, so a 9x9 scan uses 81 credits. Our listing's example is 10 keywords on a 9x9 grid, four times a month, which comes to 3,240 credits for one location before any Falcon AI reports at 25 credits each. Monthly credits expire. Starter is $24.99/mo for 7,500 credits.
Geo-grids fit single-area businesses whose customers come from a few miles around (trades, clinics, restaurants) and local SEO agencies that already sell grid reports for Google Maps. They fit less well when you need daily data across dozens of locations, since a full grid on every keyword quickly runs into credit limits.
Coordinate targeting
Coordinate targeting asks from a point instead of a city label. Promptwatch's local GEO/SEO targeting, added in October 2026, pins a monitor to exact coordinates with a radius between 20 m and 5 km. ChatGPT, Google AI Overviews and Google AI Mode then answer as if the question came from that spot. Other models on the monitor fall back to country, state or city, so it's worth knowing which engines actually change behaviour. It's on Business, Agency Scale and Enterprise, and none of those plans has a free trial.
Think of it as a single grid point that you track every day, rather than a whole grid you scan now and then. You lose the map. You gain a daily trend for the exact places that earn money, and you keep everything else the platform does on that monitor: citation analytics, competitor share of voice, and the link to crawler logs and AI-referred conversions.
It fits multi-location brands where two branches in the same city serve different neighbourhoods, and agencies whose clients ask "what does ChatGPT say to someone near our shop?" It's overkill for a business whose customers pick by city.
Picking the right mix
Start with the question you need to answer.
- "How do our locations compare city by city?" Use city tracking.
- "Which parts of town do we lose?" Run a geo-grid, at least once.
- "What do customers near our flagship store see every day?" Use coordinate targeting on that store.
Then check which engines matter to your customers. If Perplexity, Copilot or Meta AI are on the list, a method limited to ChatGPT and Google's AI surfaces won't cover them, and you'll want city-level monitors on those engines as well.
Whichever you pick, the visibility number is only half the job. When a location loses an answer, you'll want to know whether GPTBot or PerplexityBot could fetch the location page, which sources the engine cited instead, and whether AI-referred visits from that area converted. That's why we'd put city and coordinate monitors on Promptwatch and add a geo-grid tool only if you need the map. For specific matchups see Promptwatch vs Local Falcon, and for the full field, our best local AI visibility tools.