Everything in BotScope so far starts with a brand: yours, or a client’s. You add it, choose competitors, and track how AI answers treat it over time. That’s the right question once you know who you’re measuring.
There’s a question that comes before it, and we kept hearing it from agencies: who does AI recommend in this market at all? Before a pitch, before entering a new region, before deciding which competitors are worth tracking, you want the lie of the land. When someone in Bath asks ChatGPT for a solar installer, which names come back? Is it the same list in Cardiff? And when they ask how much panels cost, which websites does the answer lean on?
Research studies answer that. Pick a sector, a country and the towns you care about, and BotScope writes and runs the questions, then shows you every business AI recommends and every website it cites.
TL;DR
- No brand needed. A study measures a market, not a company. Nothing to configure beyond the sector.
- Market, then town. Choose the country the answers are observed from, and add locations to compare towns or regions side by side.
- AI drafts the study for you. It suggests the services and research topics to cover, then writes the prompts. You review and edit every one before anything runs.
- Sized for real research. From a 25-prompt sample to 500 prompts, across ChatGPT, Google AI Overviews, Gemini and Copilot.
- Brands AI recommends. Every brand named in the answers, not just the ones you thought to track, with how often each is named, its median rank, and how each assistant differs. Filter by location or service to see who leads where.
- Sources. For the research questions, which websites the answers cite and what kind of sites they are, with every cited page a click away.
- An email when it’s ready, with the headline brands and sources.
- In the API and MCP. Studies, their brand rankings and their sources are all available to your own tools.
Pick a sector and the country to observe AI answers from. Add locations to compare towns or regions, and choose how big the study should be.
One per line. Each service is asked about in each location, so you can compare towns.
Woven into some prompts where natural.
Each answer is observed from the chosen country. One credit per prompt per assistant: this study costs about 200 credits per run.
How a study is built
You start with four things: a sector (say, home energy installers), a country, optional locations inside it (Bristol, Bath, Cardiff) and a size.
From those, BotScope suggests two lists. Services are the things people hire for: solar panel installers, home battery installers, EV charger installers, heat pump installers. Topics are the things people research before they hire anyone: what panels cost, whether a battery is worth it, how to choose an installer. Edit both lists freely; they’re suggestions, not a template.
Then it writes the prompts. Each service is asked about in each location, as a request for a top-ten list (“Who are the 10 best solar panel installers in Bath?”). Each topic becomes a set of research questions. Prompts are tagged by Service and Location as they’re written, so the results can be split either way the moment the study finishes.
About 60% of a study is brand-list prompts and 40% research questions. Before anything runs you can read every prompt, rewrite or disable any of them, add your own, or import a CSV. The cost is shown up front: one credit per prompt per assistant, exactly like a project scan.
Brands AI recommends
The headline of a study is a leaderboard of every brand named in its list answers. Not only the competitors you already know about: every business the assistants name, with name variants merged, so “Sunlark Solar Ltd” and “Sunlark Solar” count once.
For each brand you get the share of list answers that name it, its median position in those lists, and the same share per assistant. Then pick a location or a service, and the table re-ranks for just those answers. The picture usually changes.
| # | Brand | Named in | Median rank | Google AI Overview | ChatGPT |
|---|---|---|---|---|---|
| 1 | Greenstead Home Energy +1 | 57% 69 ans · 45 prompts | #2 | 40% | 75% |
| 2 | Sunlark Solar +2 | 32% 38 ans · 19 prompts | #1 | 32% | 32% |
| 3 | Corvid EV +1 | 23% 28 ans · 14 prompts | #1 | 23% | 23% |
| 4 | Fernway Renewables | 23% 28 ans · 14 prompts | #1 | 23% | 23% |
| 5 | Kestrel Solar +1 | 22% 26 ans · 13 prompts | #1 | 22% | 22% |
| 6 | Halden Electrical | 22% 26 ans · 19 prompts | #2 | 32% | 12% |
| 7 | Brightmoor Energy | 20% 24 ans · 12 prompts | #5 | 20% | 20% |
Showing the top 7 of 47 brands
Across the market, the national installer leads: Greenstead is named in 57% of list answers. The assistants disagree, though. ChatGPT names it in 75% of answers and AI Overviews in 40%, while Halden is named nearly three times as often in AI Overviews as in ChatGPT.
That last part is the reason studies support locations at all. A market-wide ranking is an average, and averages favour whoever is present everywhere: usually the national player. Local reality looks different. The installer that leads in Cardiff ranks fifth across the market, and if you’re pitching a Cardiff business, it’s the competitor that matters most.
Which websites the answers cite
Brand lists show who gets recommended. The research questions show something just as useful: where AI gets its information about your market. A study charts every citation by type (government, organisations, news and media, reviews and directories, forums, video, and businesses’ own websites), then breaks it down topic by topic, with the sites cited most and how often.
Read across the topics and you get a practical map. Some questions will always be answered from official sources. Some are won on review platforms. Some are open to anyone who publishes something genuinely useful. That’s the difference between a PR target list and a content plan.
The same view is now a Sources tab in every project as well, covering your informational (Level 4) prompts, where it also shows how often answers cite your own site.
Who it’s for
- Agencies sizing up a prospect or a sector. Run a study before the pitch and walk in knowing who AI recommends in the client’s market, town by town, and which sites shape those answers.
- Businesses expanding into a new area. See who already owns the AI answers in the towns you’re moving into, before you’re there.
- Content and PR teams. Find the publications, directories and forums the assistants actually cite in your sector, then go and earn a place in them.
- Anyone choosing what to track. A study is a fast way to find the competitors worth adding to a project, including the ones you hadn’t heard of.
The honest limitations
Locations come from the question, not the device. Answers are observed from the country you choose. A study compares towns by asking about each one by name (“in Bath”), which is how most people ask. It does not simulate someone physically standing in Bath.
A run is a snapshot. AI answers vary from one day to the next, so treat a single run as a reading, not a verdict. Run the study again and each run is kept; with several runs, the leaderboard shows the median across them, and you can pick any run or date range.
Name merging is deliberately simple. We merge on case, punctuation and legal suffixes such as Ltd and LLP. “Sunlark Solar Ltd” joins “Sunlark Solar”; “Sunlark” on its own stays separate. Hover over a brand to see the variants we combined.
Four assistants, not five. Studies use the assistants we can observe from inside the market you choose, with their sources: ChatGPT, Google AI Overviews, Gemini and Copilot.
A study doesn’t score anyone. There’s no brand of your own, so there’s no visibility score. To track a brand over time, add it as a project; the study is how you decide what that project should watch.
Big studies take time. A small study finishes in minutes. A 500-prompt study across several assistants can take an hour or more. Studies also wait their turn behind scheduled project scans, so your regular tracking is never held up. You’ll get an email when the study is ready.
Plans and credits
Every plan can run studies; the plan sets the largest size.
- Trial: one Sample study (25 prompts).
- Starter: up to Standard (100 prompts).
- Pro: up to Large (250 prompts).
- Scale and above: up to Very large (500 prompts).
A study costs one credit per prompt per assistant, so a Standard study on ChatGPT and Google AI Overviews is 200 credits a run. Answers that fail to arrive aren’t charged, and they’re retried through the rest of the day, the same as a scan. If running a study would leave you without enough credits for your scheduled project scans this month, BotScope warns you before it runs, so you can buy a credit pack or run it on fewer assistants or prompts.
In the API and MCP
Four endpoints join the public API: list your studies, read one, and pull its brand leaderboard or its sources, with the same grouping by location, service or tag as the app. A fifth returns any project’s Sources view. The MCP server has matching tools (list_studies, get_study, get_study_brands, get_study_sources and get_sources), so you can ask Claude which installers lead in Cardiff and which sites it should be pitching to.
FAQs
Do I need a project or a brand to run a study? No. A study stands on its own. You don’t need a brand, a domain or any competitors.
Can I compare towns that aren’t in my country? Each study covers one country, and its locations sit inside it. To compare markets, run a study for each.
Can I change the prompts after the study has run? Yes. Edit, add or disable prompts at any time, or rewrite them from new lists of services and topics, then run the study again. Earlier runs stay in its history.
What does the email include? How many answers were collected, how many brands were named, the five brands named most and the three most-cited sources, with a link to the full study.
Does running a study affect my projects? No. Studies sit on their own Research page, separate from your projects and reports, and they queue behind your scheduled scans. The only thing they share is your credit balance, and BotScope warns you before a study would eat into what your scheduled scans need.
AI assistants are already deciding which businesses get recommended in every town, for every service, every day. Most businesses have never seen the list. Now you can see the whole of it, for any market you choose, before you’ve written a single tracking prompt.