# How to Measure Real Estate AI Visibility

> Sellers ask ChatGPT who should sell their house and pick from the names it gives. Here is how to log who gets named in each of your markets, which page came with each name, and who owns the number.

By Vlad Shvets, Partner @ Qvery · Published 2026-09-04 · Updated 2026-11-02 · 19 min read
Canonical: https://qvery.ai/blog/how-to-measure-real-estate-ai-visibility/

Sooner or later an agent forwards you a screenshot. They asked ChatGPT for the best realtor in town, and the answer named three teams from across the street and none of yours.

The screenshot can't tell you what you need to know: whether that's every answer or one of them, whether it happens in your other markets, and why those names.

It's worth knowing, because most sellers don't shop around. In Zillow's 2026 report on home sellers, **84% of sellers use AI**, about two in three of them while selling, and **67%** of sellers who used an agent contacted only one.

Zillow doesn't say how many of those sellers asked an assistant for an agent. For one who does, and then contacts one agent, the names in that answer may be the whole shortlist.

By hand, the check is one person typing one question on one laptop on one day.

After the asking, the work is extraction: turning each saved answer into rows (**who was named**, and **with which page**). An AI assistant does that now, and one person checks its rows.

I ran it on Charlotte, North Carolina, on 15 October 2026: six questions a seller or buyer would type, each asked twice in ChatGPT and twice in Google AI Mode. Every firm in the tables below is a name a real answer gave, reported as it appeared.

- **24 of 24** answers named at least one agent, team or brokerage
- **13 of 24** answers named Keller Williams once its teams were counted; 4 under its own name
- **41.18%** of ChatGPT's names came with the firm's own website
- **15 of 124** rows changed by the person's check; none named a firm the answer didn't

Source: Qvery: Charlotte run, ChatGPT and Google AI Mode, 15 October 2026

The log has three columns: was your firm named, which page came with the name, and is that page right about you. Each market then gets one row on a monthly scorecard, owned by the person who keeps that market's agent profiles.

You'll build a market scorecard, then see how we handle the monthly reading at Qvery, with a person approving the AI agent's drafts.

**What you'll need**
- **Question list:** six questions per market, taken from your own inquiry emails and calls
- **ChatGPT:** any plan, or no account at all, with search on
- **Google AI Mode:** google.com/ai in a browser window signed out of Google
- **Spreadsheet:** one file per market, with an Answers tab and a Names tab
- **Assistant:** any chat assistant that accepts long pastes (we used Claude)
- **Roster:** every agent's team, office, brokerage, phone and profile links, kept by one person

Time: time your first market: 24 asks, then one read of every row

This measures the answers to hire-an-agent questions, one market at a time. It doesn't attribute leads, and it doesn't tell you why an engine picked one firm over another.

## Write Six Questions a Seller Would Type, With the City in Every One

**Use your own inquiries**, not a list of popular searches. The first message a seller sends through your website, the line a buyer opens with on the phone, the situation your agents hear at listing appointments: an inherited house, a job move, a first purchase.

Six per market is enough to start: two seller questions, two buyer questions, two situations your firm handles well. These are Charlotte's:

- Who is the best real estate agent to sell my house in Charlotte NC?
- Which real estate brokerage should I list my home with in Charlotte?
- Best realtor in Charlotte for first-time home buyers
- I'm relocating to Charlotte for work. Which real estate agent should I use?
- Best real estate team in Charlotte for selling an inherited house
- Top real estate brokerages in Charlotte NC

**Include the city.** OpenAI's ChatGPT search help page says an approximate location from your IP address may affect local results.

**Leave your firm's name out.** A question that names your firm asks about its reputation, and it gets its own answers from its own sources. Track it in a log of its own; mixed in, it makes your named rate look better than any seller will find it.

OpenAI's Temporary Chat help page describes the Unpersonalized setting. Ask in a signed-out session or a temporary chat set to Unpersonalized; if ChatGPT doesn't search, select search from View all tools.

In Google AI Mode, go to google.com/ai signed out of Google. Google's AI Mode help page says saved search history can personalize answers.

**Ask twice**, every question, on each engine. In our research on ChatGPT's September answers to agent-hiring questions in US cities, each asked three times, more than half the firms in its block of local business cards turned up on only one of the three asks.

**Most Firms ChatGPT Listed Showed Up on Only One of Three Identical Asks** (% of distinct firms listed)

52.74% of the firms ChatGPT listed for a city question appeared on only one of three identical asks; 20.55% appeared on all three.

| Label | Value |
|---|---:|
| On one ask only | 52.74% |
| On two of the three asks | 26.71% |
| On all three asks | 20.55% |

Firms ChatGPT listed in its block of local business cards for agent-hiring questions naming a US city, each question asked three times; share of distinct firms by how many of the three asks showed them. September 2026. Source: Qvery research: ChatGPT answers to agent-hiring questions naming a US city, September 2026

The r/realtors discussion below separates seeing a referral from knowing which questions bring your firm into answers:

**Tips**

> One of my agents got referred through chatgpt. They have no idea what causes it.
> ([r/realtors · Aug 2026](https://www.reddit.com/r/realtors/comments/1vrrafp/anyone_tracking_whether_they_actually_show_up/))

> It is mainly one broker that comes up that has a big marketing ad spend, buys leads, and does a bunch of business.
> ([r/realtors · Aug 2026](https://www.reddit.com/r/realtors/comments/1vrrafp/anyone_tracking_whether_they_actually_show_up/))

> I'd build a fixed set of maybe 15–20 real buyer/seller questions, run them in fresh sessions across ChatGPT/Perplexity/Google on a regular cadence, and log whether you're mentioned, what source gets cited, and whether there's actually a link.
> ([r/realtors · Aug 2026](https://www.reddit.com/r/realtors/comments/1vrrafp/anyone_tracking_whether_they_actually_show_up/))

Save each whole answer and its source titles and links in the Answers tab. Give it an ID made from market, engine, ask, and question number, as shown below.

**The Answers tab, one row per answer**

| Column | What goes in it | Example from Charlotte |
|---|---|---|
| Answer ID | market, engine, ask, question number | CLT-AIM-2-Q4 |
| Engine | ChatGPT or Google AI Mode | Google AI Mode |
| Date | the day you asked | 2026-10-15 |
| Question | exactly as typed | I'm relocating to Charlotte for work. Which real estate agent should I use? |
| Answer | the full text, cards and lists included | the whole answer, pasted |
| Sources | every link the answer showed, with its title | realtrends.com: 2026 Best Real Estate Agents in Charlotte |

When the asking is done, the tab holds 24 rows for six questions, one per question, engine and ask.

We saved Charlotte's answers through a collection service rather than by hand, asking from the US in English with search on, and keeping each answer whole with its cards, links and source titles.

Its Google AI Mode answers held two to five source links each and ChatGPT's three to eleven, so everything below about pages reads one engine at a time.

> 📌 **Three things that look like mistakes and aren't** 
> - ChatGPT often lists firms with a star rating, a review count and a phone number. Those come from business listings; keep every one, they're names.
> - Google AI Mode's source list includes Google business listings titled with a firm's name. Save the title; it tells you which firm the listing belongs to.
> - Some links are plainly wrong: one Charlotte answer linked the word "Charlotte's" to a restaurant review in Danville, Illinois. Save it as shown and judge it later.

## Column One: Did the Answer Name Your Firm, or One of Your Agents?

The first number on the scorecard is how many answers named you. It needs every name in every answer in one table you can sort, and building that table is the assistant's part.

We first asked for names and sources as a table, using six Charlotte answers at a time. The output couldn't be counted:

- Two tables per answer, 48 for 24 answers, and the layout changed between batches, so nothing pastes into one sheet.
- Names in one table and sources in the other: no row said which page came with which name.
- A person and their team on separate rows, so a count of names counts an agent and the agent's own firm as two firms.

The prompt below keeps one row per firm, its pages beside it, and the same columns in each batch.

**The log prompt**

```
You are filling a log of AI answers about real estate agents. Below are saved answers. Each one starts with its ID, the engine, the question, the answer as shown, and the list of sources the answer showed.

For every answer, write one row for each real estate agent, team, brokerage or other business that the answer puts forward as someone to hire, interview or contact. Columns separated by tabs, in this order:

1. Answer ID
2. Order: 1 for the first name in the answer, 2 for the next, and so on
3. As written: the name exactly as the answer writes it
4. Kind: agent, team, brokerage, or other (for example a cash buyer or an estate sale company)
5. Agent: the person's name, if one is named; several separated by "; "
6. Team: the team or group name, if one is named
7. Brokerage: the brokerage the answer ties the name to, if any
8. Pages: every web address linked inside or directly under this name's entry, plus any source in the answer's source list whose title is this name; several separated by "; ". Write "business listing" when the name appears as a business card (a star rating, a review count, an address or a phone number) or its only page is a Google business listing. Write "none" when nothing is shown for it.
9. Own site: "yes" if one of the pages is the named agent's, team's or brokerage's own website (an office page on its franchise's site counts; the franchise's national homepage, such as kw.com or remax.com, does not); "no" if none is; leave blank for "business listing" and "none"

Rules:
- One row per firm. If one entry names two firms, write two rows.
- The websites the answer used as sources are not names (for example Zillow, Realtor.com, Redfin, HomeLight, RealTrends, U.S. News, Reddit, Yelp, or a "best agents" list), unless the answer recommends hiring that company itself.
- If an answer names no one, write a single row: the Answer ID, then "0", then "no names".
- Copy names as written. Do not merge spellings of the same firm across answers.
- When you are not sure about a cell, write "unclear". Do not guess.
- Output only the rows. No header, no notes.
```

One firm means one row. An entry that names an agent, her team and her brokerage is a single row, with the three in their own columns; an entry that names two separate firms is two rows.

Paste the prompt, then six saved answers with their IDs, engines, questions, and sources. Copy the output into the Names tab; these are three of its nine columns:

**The Names tab for answer CLT-CG-1-Q1, three of nine columns, agents' names withheld**

| Name, as the answer gave it | Pages | Own site |
|---|---|---|
| Stone Realty Group agent | realtor.com agent directory | no |
| The Redbud Group agent | zillow.com agent directory | no |
| The Finigan Group agent | thefinigangroup.com | yes |
| Helen Adams Realty agent | realtor.com agent directory | no |

**Then read every row** beside its answer. That part stays with a person.

On the Charlotte run we read all 124 rows the first version of this prompt returned and changed 15, one of them by splitting a two-firm row in two, so the checked log holds 125 names.

**None named an extra firm**, and none listed Zillow or a ranking site as a firm. Most of the changes were about pages:

**What the person's check changed, Charlotte run**

| What the check found | Rows | Example from Charlotte |
|---|---|---|
| A link under the name missed, or one of two dropped | 7 | Zillow's seller directory, beside an agent's own site |
| A Google listing in the sources, not tied to its firm | 3 | Dickens Mitchener's listing, titled with its name |
| A franchise's national homepage called the office's site | 3 | kw.com, for Keller Williams SouthPark |
| Two firms written as one row | 1 | Corcoran HM Properties and Ivester Jackson Christie's |
| Agents named in the entry, left out | 1 | two Dickens Mitchener agents |

Run again on the same 24 answers, it got 12 of the 15 corrected rows right on its own. That checks the new lines; it doesn't predict how the prompt does on your market.

It still missed two links inside sentences and an unlinked ranking, and misclassified Compass's own site as a franchise homepage. A person must check every row.

Add one column yourself: **Firm**, the single name you'll count under. Stone Realty Group appeared three ways in the Charlotte answers, under an agent's name, under its own and under its team's, and all three count as one firm.

Then count a second way, with your **teams under your brokerage**, because the answers often name a team rather than its brokerage. In Charlotte, Keller Williams was in **13 of the 24 answers** once its teams were counted with it, and in 4 under its own name.

Your **named rate** in a market is the share of answers that named your firm, one of your teams or one of your agents. Here is the top of Charlotte's table, counted at the level the answers named each firm:

**The most-named firms in Charlotte, 24 answers**

| Firm, as the answers named it | Answers, of 24 | ChatGPT, then AI Mode |
|---|---|---|
| Stone Realty Group | 10 | 5 and 5 |
| High Performance Real Estate Advisors | 7 | 1 and 6 |
| The Redbud Group | 7 | 3 and 4 |
| Dickens Mitchener | 6 | 2 and 4 |
| Andy Bovender Team | 5 | 1 and 4 |
| Corcoran HM Properties | 5 | 2 and 3 |

## Column Two: Which Page or Listing Came With Each Name?

The page that came with a name is the part your team can **work on**, and in a city those pages are spread across many sites. Our research on agent-hiring questions found the answers citing local firms' own sites more often than the portals:

**Local Firms' Own Sites Were Cited More Often Than the Portals** (% of answers that cited any source)

Local agents' and brokerages' own websites were among the sources in 64.1% of city answers, ahead of the national portals at 36.75%.

| Label | Value |
|---|---:|
| Local agents' and brokerages' own sites | 64.1% |
| National portals and national brokerage sites | 36.75% |
| Agent rankings and industry press | 34.19% |
| Agent-matching and review sites | 17.09% |
| Google's own pages and business listings | 15.38% |
| Forums and review communities | 10.26% |

Share of answers that cited each kind of site, of the answers that cited any source; an answer can cite several kinds, so the bars don't add up to 100. ChatGPT and Google AI Mode together, agent-hiring questions naming a US city, September 2026. Source: Qvery research: ChatGPT and Google AI Mode answers to agent-hiring questions naming a US city, September 2026

The local layer was a different set of firms in every city: the most frequent single local site appeared in **4.27%** of answers that cited anything.

Your firm can also be named without its site being cited. Of the firms ChatGPT put in its business-card block in September, **10.47%** had their own website among the sources of the answer that named them.

Charlotte shows the same split, read one engine at a time. **All 51 of ChatGPT's names** came with at least one page, and the firm's own site was only one of the kinds:

**Fewer Than Half of ChatGPT's Names Came With the Firm's Own Site** (% of ChatGPT's names)

41.18% of ChatGPT's Charlotte names came with the firm's own website, 25.49% as a business listing and 17.65% with a Zillow or Realtor.com agent directory page.

| Label | Value |
|---|---:|
| The firm's own website | 41.18% |
| A business listing | 25.49% |
| A Zillow or Realtor.com agent directory | 17.65% |

Share of the 51 names ChatGPT gave in the Charlotte run that came with each kind of page; a name can come with more than one. Six questions, each asked twice, 15 October 2026. Source: Qvery: Charlotte run, ChatGPT, 15 October 2026

Google AI Mode's saved answers carried fewer links, so read its count on its own: 26 of its 74 names came with a page, and **16 of those 26** were the firm's Google business listing.

When AI Mode pointed somewhere for a firm in this run, it was usually that listing, which makes the listing the first page to check.

Count each roster firm's state per answer: the Names tab supplies the first three states below, the Sources column the fourth, and your roster the fifth. These are what the answer displayed, not proof of what the engine used.

**The five states of a name, for your own firm**

| What the log shows | What to do | How you know it worked |
|---|---|---|
| Named, with your own page | Keep that page current: teams, specialties, recent sales | The same page comes with your name next month |
| Named through someone else's page or listing | Check that page in column three; it speaks for you | The page states your current facts |
| Named with nothing shown | Check your Google listing and the pages carrying rivals' names | A page appears beside your name on a later ask |
| Your page in the sources, another firm named | Read your page as a seller would: what, where, for whom | Your firm shows up in answers citing that page |
| Not named, not in the sources | Read the pages that came with the names you lost to | A first appearance on both asks, not just one |

Stone Realty Group's Google listing sat in the sources of three Google AI Mode answers that recommended other firms. A local team's post ranking Charlotte's top five listing agents was cited in a ChatGPT answer that named three firms, none of them its author.

> 💡 **What isn't a problem** 
> - Being named through your Zillow or Realtor.com profile, if the profile is right. It's still your name in the answer.
> - A name that arrives as a business listing with no link. The listing is a page too; check it in column three.
> - A firm that turned up on one ask out of four. That's the variation the double asking is there to absorb.

## Column Three: Is That Page Right About You?

The pages in column two are the ones the answers show when they describe you, and an answer can **repeat a page**.

In one Charlotte answer about an inherited house, ChatGPT recommended an estate-sale specialist and then told the seller to ask about her Charlotte experience, because some of her published testimonials concern New Jersey properties. The warning came from her own page.

So the third column checks every page that carried one of your names against your roster. These are the fields that drift, and who fixes each:

**What to check on each page, and who fixes it**

| Field | How it drifts | Who fixes it |
|---|---|---|
| Brokerage | an agent moved; the page shows the old firm | the agent, then the records behind the profile |
| Team | a team renamed or split; the old name stays | whoever keeps your team pages |
| Office and phone | an office closed or moved | your web person, the same day |
| Specialty | the page describes work the agent no longer does | the agent, signed off by the team lead |
| Agent | someone who left is still listed under you | the roster keeper |

**For your own pages**, paste the text and roster row into the assistant. Ask it to quote each mismatched fact and draft a correction that changes only that fact.

Have a person open outside profiles beside the roster. Zillow's terms bar automated queries that obtain information from its services; Realtor.com's terms bar automated collection without written permission.

What a portal profile says about an agent comes from your own records, so the fix is a records job inside your firm, covered in our piece on [what your brokerage controls on the portal pages AI answers cite](https://qvery.ai/blog/zillow-ai-citations-statistics/).

**A person makes the fixes.** Let the assistant draft the mismatches and the corrected wording. Keep these with a person: anything published under an agent's name, any reply to a review, any change to a portal profile or a Google listing, and anything that costs money.

Check that it held a month later: the same page, read again, shows no mismatch, and the answers that come with it repeat the corrected facts.

## Give Each Market an Owner and One Row in the Monthly Review

So each market gets what a territory gets: **a fixed question list**, **one number a month** and **one owner**. The owner is whoever keeps that market's agent profiles, because column three ends on their desk anyway.

Read the scorecard at the market's monthly listings and closings meeting. Here is the arithmetic for Charlotte's most-named firm:

**One market's scorecard row, counted for Charlotte's most-named firm in this run**

| Line | How to count it | Stone Realty Group, Charlotte |
|---|---|---|
| Named rate | answers naming the firm, a team or an agent | 10 of 24 |
| By engine | the same count on each engine | 5 on ChatGPT, 5 on Google AI Mode |
| With its own page | answers where its own site came with the name | 3 of the 10 |
| Pages that carried it | the sites in its rows' Pages column | own sites, Realtor.com and Zillow directories, RealTrends, U.S. News, Google listing |
| Rivals named most | the other firms with the highest named rate | High Performance and The Redbud Group, 7 each |
| Change since last month | names gained and lost on the same questions | first month |

Read the changes with the September repeat asks in mind, and take this as an operating rule, not a measured threshold. A firm that shows on one ask out of four is **weather**. A firm named on both asks on an engine, two months running, is a pattern in your market.

Move the monthly work on patterns. Fix a wrong fact on a page the day you find it, however often that page shows up.

Four asks per question a month tells weather from a pattern, but slowly, over months. To see the pattern sooner, the same questions have to run more often than a person will ask them.

GEO Agent: https://qvery.ai/geo-agent/

## Have an AI Agent Build Each Market's Log Once the Month's Answers Are In

Time the first month's asking, pasting, and checking per market. The AI agent takes over the repeated reading; a person keeps the check.

The AI agent doesn't do the asking. A person does it in a clean window, or [Qvery's GEO Agent](https://qvery.ai/geo-agent/) does it every day. The AI agent starts once a market's answers for the month are saved, and it reads four things, all yours:

**What the AI agent reads**

| Input | Where it lives | What it takes from it |
|---|---|---|
| The month's answers | the market's Answers tab | every name and every link |
| Your roster | the roster sheet | who works where, on which team, under which brokerage |
| Last month's log | the Names tab and the scorecard row | what changed since then |
| Your own website | your team and agent pages | the facts it checks against the roster |

It runs once per market, the morning after that market's Answers tab is complete, and applies five rules:

1. **Write the log** One row per name, every page under it, by the log prompt's rules, into the Names tab.
2. **Roll each name up to your roster** Agent to team, team to brokerage. A name that half-matches, such as an agent who left or a misspelling, goes to a person as a question, never a guess.
3. **Put every name in its state** The five states above, so the scorecard counts the same way every month.
4. **Read your own pages** Every page of yours that came with a name, against the roster row, with a drafted correction for each mismatch.
5. **List the pages it won't open** Portal profiles, Google listings and rankings that carried your names, each with its roster row beside it, for a person to open.

It returns the Names tab, scorecard, drafted corrections, and outside pages for a person to open.

![One market's month in five steps: answers saved, the AI agent writes the log and reads your own pages, a person checks and approves, one row goes to the market review, then the loop repeats.](https://qvery.ai/blog/how-to-measure-real-estate-ai-visibility/illustration-monthly-log-loop.webp)

*Everything the AI agent writes passes the person in step four before it reaches the review.*

> ✋ **What a person keeps** 
> - Every row of the Names tab, read beside its answer, before the scorecard counts it. On Charlotte, that read changed 15 of 124 rows.
> - Every drafted correction, approved by the profile keeper before your web person publishes it.
> - Anything under an agent's name, any review reply, and any change to a portal profile or a Google listing.

Judge it on two numbers you can compare month to month: the minutes a person spends on the check, and how many corrections are still open a week later. The month you built by hand is what you compare against.

Not all of it needs an AI agent. Counting rows by state and working out a named rate per market is a spreadsheet formula once the Firm column is filled.

Before building it, put every agent's team, office, brokerage, market, and profile links in one roster sheet, with one person responsible for it.

## How Qvery Handles Your Market Scorecards

Our GEO Agent follows your brokerage into buyer and seller searches. It creates questions using your brand alongside your competitors and industry. For your selected countries, it asks Google AI Mode and ChatGPT those questions daily, keeping the answers in full along with all citations.

Its topic and country views show visibility alongside competing firms, differences in sentiment, cited sources, and share of voice. We examine those records together and correct the pages that misdescribe your brokerage.

We learn your profile keeper's monthly routine before building a custom AI agent with connections into the software your offices use. When the answers arrive, it prepares the log and proposed corrections on autopilot. Your profile keeper approves the work.

Within two weeks, your profile keeper has the first roster-check version running, plus training to operate it. Our weekly count covers hours saved preparing the scorecard and errors avoided when matching names, affiliations, and page facts.

Bring the sheet you used for your first market so we can follow the work from answer to correction.

Have Us Handle Your Brokerage's Monthly Answer Log: https://calendly.com/qvery-vlad/30min

## Assign the Missing Names to Your Profile Keeper

When the first log is checked, sort the Names tab by your own agents. You'll see which of your people the answers already know, which page came with each of them, and which agents who close every month aren't in a single answer.

That last list goes to the person who keeps the profiles, with a date beside each name. Next month's log shows whether any of them is named.

Route Property Inquiries to the Right Office: https://calendly.com/qvery-vlad/30min

## Frequently asked questions



### How do I check whether ChatGPT recommends my brokerage in my city?

Ask the questions your sellers and buyers type, with the city in each one, in a signed-out session or a temporary chat set to Unpersonalized. Ask each one twice, save the whole answer with its sources, and count the answers that name your firm, a team of yours or one of your agents.

### Does ChatGPT use my location when I ask for a realtor?

OpenAI's ChatGPT search help page says an approximate location from your IP address may affect local results. Put the city in each tracked question so your head office's location does not stand in for the market you want to measure.

### Is being cited by an AI answer the same as being recommended?

A cited page is a source shown by the answer; a recommendation names a firm to consider. In Charlotte, Stone Realty Group's Google listing appeared among sources in answers recommending other firms. Keep names and cited pages in separate columns.

### Who in a brokerage should own AI visibility?

Give each market to the person who keeps its agent profiles and listings, because corrections land on their desk. The market leader should review the named rate, cited pages, leading rivals, and changes at the monthly listings and closings meeting.

### How often should a brokerage measure its AI visibility?

Use the same questions monthly, twice per engine, and treat the first month as a baseline. As an operating rule, look for a firm on both asks on an engine over two months before treating its appearance as a pattern. Correct wrong facts whenever you find them.

### Can an AI agent run this for us?

An AI agent can prepare the log, match names to your roster, and draft corrections for your own pages. A person still checks those rows against the saved answers and approves changes before publication.

### Will fixing these pages get my brokerage recommended more often?

Nobody can promise that, and this log doesn't prove cause. It gives you the pages the answers cite in your market and whether each one is right about you. Fix what's wrong, ask the same questions next month, and the log shows whether the names moved.

### Which questions should a brokerage track?

Take questions from inquiry emails, buyer calls, and listing appointments. Start with seller questions, buyer questions, and situations your firm handles, such as relocation or an inherited house, and put the city in each. Keep questions naming your firm in a separate log.

### What should we save from each AI answer?

Save the full answer, including cards and lists, plus every displayed source link and its title. Record the engine, date, exact question, and an ID connecting the answer to its market and ask number. Keep that original beside the extracted rows for checking.

### How do we count teams under our brokerage?

Add a Firm column to reconcile different names for the same firm, then count again with teams grouped under your brokerage. Use your roster to resolve those relationships. Keep the count as answers naming you, so several people from your firm in one answer do not inflate it.

### Does a portal profile count as visibility?

Being named through a Zillow or Realtor.com profile still puts your name in the answer. Check that profile against your current roster, and record it separately from an appearance with your own website. The person responsible for the profile handles corrections.

### What can a market scorecard tell us about leads?

The scorecard records who appeared and which pages accompanied each name; it does not attribute leads. It also cannot explain why an engine chose a firm. Use it to assign page corrections and compare later answers to the same questions.

### What does Qvery do after the scorecard is built?

We read the findings with your team and address the pages and facts the answers cite. For repeated roster checks, we build a custom AI agent that prepares the log and corrections in your existing systems, with your profile keeper approving its work.