Start with the decision each location needs to make
Consider a fictional repair company with branches in Pasadena and Long Beach. The table below is an illustrative specification, not a measured test or a prescribed question panel.
Read each row with its scope and evidence.
| Decision | Example question | What a useful record must establish |
|---|---|---|
| Is the branch described correctly? | “Does Harbor Repair’s Pasadena branch offer Saturday appointments?” | Whether the answer identifies the intended branch, what it claims and the exact source URL. This is a named-business accuracy check. |
| Is the business considered for a local need? | “Who repairs washing machines in Pasadena on Saturdays?” | Which businesses are explicitly suggested for that need; whether the answer’s location and service details fit. This is an unbranded discovery check. |
| What does a nearby customer encounter? | “Who repairs washing machines near me on Saturdays?” | The observed or configured location, collection surface and available context. Without that recorded context, another analyst cannot establish which geographic observation the result represents. |
Keep these three results separate. Correct information after naming your branch does not demonstrate that an unbranded customer will encounter it. A corporate mention does not establish which branch was recommended, and a link alone does not tell you that an appointment was available.
The practical first step is a branch register: business name, branch label, address or service area, and the canonical page for that location. Decide how the report will handle ambiguous company mentions, multiple branches and service-area businesses before comparing locations. This is an editorial measurement specification, not a claim about a particular vendor’s detection system.
Ask what the location setting actually controls
There are at least three separate inputs to inspect: the place named in the question, the geographic configuration used for collection, and the context available in the experience being observed.
OpenAI’s web-search API documentation describes an approximate location setting with country, city, region and timezone fields. That documents a configurable API input; it does not establish that a vendor uses it or that it recreates a person standing at a particular address. OpenAI web-search documentation
The consumer experience has other context. OpenAI says ChatGPT may use approximate IP location, optional device location and relevant saved memory in local search. A named-city prompt and a signed-in customer’s “near me” query therefore should not be treated as interchangeable observations. ChatGPT search and location
Ask the supplier to name the collection surface and explain how its location setting is applied on each required engine. Record unsupported or undisclosed precision as such. A country setting may satisfy a national-market brief; it does not, on its own, substantiate a neighborhood-level claim.
What current documentation establishes
These examples show why “local coverage” needs a specification. They are selected for public documentation about location controls, not as a complete market shortlist or a performance comparison. Nothing in this table establishes address-level fidelity.
Read each row with its scope and evidence.
| Supplier | Documented location behavior | What still needs to be established for your purchase |
|---|---|---|
| Peec AI | Its help center says each prompt runs from one country. Countries are set per prompt; a project can cover multiple markets within its plan’s country limit. A blank country in a CSV uses the project default. | Whether the available country-level coverage serves the branch decision; the applicable plan limit; and how the configured location appears in the exported result. Source |
| OtterlyAI | Its help center documents selecting a country for a prompt and comparing the same prompt across countries. | The engine-country combinations you require, the location detail recorded with each answer and any more precise control needed for your use case. Source |
| Ahrefs Brand Radar | Its custom-prompt guide documents one or more locations for a check and defines a check as one prompt execution × one model × one location. | Which location options the selected model offers, how branch outcomes can be reviewed and how the proposed location matrix affects the quoted workload. Source |
| Gist GEO | This guide does not establish Gist’s commercial location granularity or branch-reporting entitlement. | Require the same written scope and inspectable sample record before treating Gist as a fit. Ownership is not evidence that these requirements are met. |
An unanswered requirement is a verification gap, not proof that a product lacks the feature. Conversely, a product page that mentions “local” is not enough to mark every requirement as satisfied.
Bring a small location matrix to the demo
Ask for a saved, non-confidential example or an agreed demonstration that separates the inputs. There is no need to activate a large paid panel to understand the contract.
Scroll horizontally to compare fixed inputs, deliberately changed inputs and the evidence each comparison can show.
| Comparison | Keep fixed | Change deliberately | What it can help you inspect |
|---|---|---|---|
| Named-place wording | Surface, collection configuration, language and timing | Pasadena versus Long Beach in the question | Whether the record preserves the exact geographic intent and identifies the corresponding branch. |
| Configured location | Exact question, surface and language | The supported location setting | Whether the configuration is recorded and what the captured response actually contains. It does not isolate a geographic effect from every other source of variation. |
| Named versus unbranded question | Intended market and service need | Whether the company is named | Accuracy after an explicit lookup versus discovery without the brand cue. Report separately. |
For each row, request the full answer, actual cited URLs, timestamp, configured location, surface and collection outcome. Ask how failures, absent AI answers and ambiguous branch names appear. Do not silently count a failed capture as a branch that lost visibility. Our sample-record guide provides the wider evidence contract.
Price the coverage you will actually use
List the required combinations of service need, location, language and engine before requesting a price. Not every combination belongs in the study: a branch that does not provide a service should not be evaluated as though it does. Keep omitted combinations visible rather than filling a grid simply because a plan has room.
The quote should distinguish configuration capacity, collection frequency, saved answers, review access and exports. A city name in five questions is not automatically five supported collection locations. Nor does a large quota establish reliable comparisons. Use the supplier’s documented billing unit instead of assuming that every vendor charges for the same thing.
Buy the scope your team can interpret. If the job is national-market research, country-level coverage may be appropriate. If the decision concerns a branch or a nearby customer, require evidence at that level and label any remaining gap. The useful report is the one that can show which business, which place and which observed answer support the decision.