When a buyer asks AI who to hire in your market, does your team show up?
Recommended First measures which real-estate teams AI recommends, compares those answers with verified production, and shows you the evidence behind the gap.
Verified production · Preserved answers · Methodology published · No ranking guarantees
Benchmark A — Greenville, South Carolina
Anonymized measured example
Verified production
- Your team
- $78.9M
- Competitor
- $33.0M
Top-5 team by volume
Outside the top 20 by volume
AI recommendations · of 256 valid captured answers
- Your team
- 0 / 256
- Competitor
- 17 / 256
You outproduce them. AI recommends them more.
- Market
- Greenville, SC · residential (anonymized)
- Benchmark date
- August 2026
- Provider
- ChatGPT (OpenAI)
- Prompts
- 64 buyer and seller questions
- Repetitions
- 4 per question
- Sample
- 256 valid captured answers
- Production source
- RealTrends 2025 closed volume
OpenAI-powered search response; exact model metadata unavailable for this benchmark. Every figure in this exhibit describes the same captured run.
What we do
Three connected things, in one measured loop.
We do not reverse-engineer a private algorithm. We reverse-engineer the observable evidence environment around AI recommendations.
- 01
Measure who AI recommends
A fixed question set is run repeatedly against ChatGPT (OpenAI). Every answer is preserved word for word and each recommendation is classified per answer.
Review the measurement methodology - 02
Investigate the evidence behind the answers
Citation Intelligence records the pages and domains that appear around those recommendations, classifies each source, and compares that evidence footprint with your own coverage.
See how Citation Intelligence works - 03
Improve legitimate coverage, then remeasure
Gaps are prioritized, corrections and additions are documented, and the same question set is rerun so the change log sits beside the result.
See how AI search optimization works
Figure 0Measure, investigate, improve — each step traceable to a preserved answer.
02The visibility gap
Market position and AI recommendations are two different things.
Plotting verified production against measured recommendation frequency isolates the case that matters: teams with real authority that the answers keep overlooking.
The addressable position
High authority, low AI visibility. The credibility already exists — the sources just don’t carry it.
Figure 01Verified authority vs. measured recommendation visibility
AI Blind Spot
Market Leader
Low Priority
AI Overperformer
Measured AI visibility →
03The evidence
Every number traces back to an answer you can read.
Questions are fixed in advance, answers are preserved word for word, recommendations are classified per answer, and the final count can be inspected one response at a time.
Benchmark B — Jersey City, NJ (anonymized)
Every number has a receipt.
Every aggregate traces back to individual captured answers. Questions are stored, answers are preserved, recommendations are classified per response, and the final count can be inspected answer by answer.
- Market
- Jersey City, NJ
- Provider
- ChatGPT (OpenAI)
- Captured
- August 2026
- Prompts
- 64 × 4 runs
- Valid answers
- 256
Figure 02Captured answer → recommendation count
- 01
Question asked
“Who are the best real estate teams in Jersey City?”
- 02
Captured answer excerpt
ChatGPT (OpenAI) · Jersey City, NJ · August 2026
For Jersey City, a few teams come up consistently. Team A is generally regarded as the strongest for both buyers and sellers, with heavy activity downtown and along the waterfront. Team B is also well reviewed for condo sales, and Team C handles a good share of high-rise listings. Larger brokerage offices in the area are another option if you'd prefer a bigger platform.
- 03
Recommendations extracted
- Team A — recommended
- Team B — recommended
- Team C — recommended
- The team (you) — not recommended
Classified per answer as a recommendation (named as who to hire) rather than a passing mention.
OpenAI-powered search response; exact model metadata unavailable for this benchmark.
- 04
Aggregate this answer feeds
4 / 256recommendations across all valid captured answers
04Why the gap exists
Three things we investigate when AI overlooks a strong team.
Whether your track record is represented, whether AI can tell who you are, and whether the sources it reads substantiate any of it.
Authority
Is your track record represented?
Your production record may be strong without being consistently represented in the sources that surface around recommendations.
Entity clarity
Does AI understand who you are?
Name variants, stale brokerage attribution and split profiles fragment one team into several.
Source coverage
Do trusted third parties substantiate it?
When independent sources don’t clearly substantiate the team, the available evidence can be thinner than the real-world track record.
The sources behind the answers
A short list of source types keeps appearing in the answers.
Across benchmark B, certain source classes repeatedly appear alongside recommended teams.
Figure 03Source classes observed in captured answers — indexed frequency
| Source class | Indexed frequency (listing portals = 100) |
|---|---|
| Listing portals | 100 |
| Brokerage sites | 44 |
| Local publications | 22 |
| Community forums | 12 |
| Video | 7 |
| The team's own site | Not cited in the captured benchmark responses |
- Unit
- Indexed source-appearance frequency (most frequent class = 100).
- Numerator
- Citations belonging to that source class within the captured answers.
- Denominator
- Citations belonging to the most frequent class (listing portals).
- Multiple citations
- A single answer can cite several sources, so citations are counted per citation, not per answer.
- Market
- Jersey City, NJ · residential (anonymized)
- Provider
- ChatGPT (OpenAI)
- Captured
- August 2026
- Sample size
- 64 buyer and seller questions × 4 per question = 256 valid captured answers
Indexed frequency expresses how often each source class appeared around recommendations relative to the most-cited class in the same captured set. One answer can cite multiple sources. Observed association only; no causal claim is made. OpenAI-powered search response; exact model metadata unavailable for this benchmark.
Citation Intelligence
See the evidence behind the recommendations.
See which pages and domains appear around AI recommendations, then compare that evidence with your own coverage.
Figure 04The Citation Intelligence workflow
- 01Prompt
- 02AI answer
- 03Citation
- 04Source classification
- 05Competitor comparison
- 06Evidence gap
- 07Action
- 08Remeasurement
- Measure who AI recommends across a fixed, repeated question set.
- Record the sources cited inside each captured answer.
- Map the domains and source types that recur behind recommendations.
- Identify entity and evidence gaps against your own coverage.
- Prioritize legitimate improvements by importance, frequency and difficulty.
- Remeasure using comparable prompts and conditions.
How results become evidence
What we measure once an engagement starts.
We don't publish case studies we can't evidence. Same questions, same repetitions, same provider — measured before and after.
01
Baseline
Recommendation frequency, competitor presence, source coverage and how accurately you're described — captured before anything changes.
02
During engagement
Every change we make is logged with the evidence behind it, so each intervention can be traced to a reason.
03
Remeasurement
The same controlled prompt set, the same repetitions, the same provider — new answers captured and counted the same way.
04
Outcome
We report what changed and what didn't. A flat number is reported as a flat number.
05Why trust this
Published method, stated limits, one name on the work.
Methodology
How the number is built.
64Buyer + seller questions
4Repetitions
256Maximum captured answers
- 01Raw answers preserved verbatim
- 02Recommendations classified per answer
- 03Compared with verified RealTrends production
- 04Every claim traceable to a stored response
Measured results and our interpretation are kept separate. Observations are recorded facts, inferences are labeled, and nothing is presented as a guaranteed ranking outcome.
The valid-answer denominator is published with each benchmark.
View the full methodologyWhat stands behind the work
No testimonials yet, and none invented. What exists is the record of the work itself.
- Verified production data
- Third-party market authority (RealTrends 2025), not self-reported volume.
- 256 preserved AI answers
- Measured across repeated runs rather than collected anecdotally.
- Published methodology
- Prompt set, repetitions, provider, capture date and denominator are stated in full. Read the methodology
- Sample audit available
- The deliverable can be inspected before any conversation happens. View a sample audit
- One client per market
- A commercial commitment, not a scarcity device: two clients in one market would compete for the same answers.
Code of practice
What we won’t tell you
- We don’t guarantee rankings.
- AI answers are probabilistic and change over time. Nobody outside the model can promise a position in them.
- We don’t call one screenshot a trend.
- Recommendations are measured across repeated prompts and repeated runs, and reported with the denominator.
- We don’t publish numbers we can’t trace.
- Every published metric connects back to a captured answer that can be inspected on request.
- We don’t invent case studies.
- Until a client engagement has a measured before and after, there is no case study to show.
- We don’t claim credit for everything that moves.
- Observation and interpretation are labeled separately. A flat number is reported as a flat number.
Who is accountable for the work
Built and reviewed by Francisco Zuluaga
Founder, Recommended First · Brooklyn, NY
I built Recommended First because real market performance and AI recommendations are starting to diverge in ways that aren’t obvious from traditional search rankings. A team can lead its market on closed volume and still be missing from the answer a buyer actually reads.
My background is in data science, so the work starts with measurement: preserve the answers, compare them with verified production, and only make claims we can trace back to the underlying data. Every benchmark I send has been read by me before it goes out. If a number in your report looks wrong, the captured answer behind it is available on request.
Research
Published benchmarks, with their limits stated.
We do not publish findings that cannot be traced to preserved evidence.
Proprietary benchmark
ChatGPT Real Estate Visibility Benchmark
The primary citable dataset: how often ChatGPT (OpenAI) recommends real estate teams across repeated captured answers, with the prompt taxonomy, share definitions, exclusion rules and source-class taxonomy published in full.
- Market:
- Greenville, SC and Jersey City, NJ · residential (both anonymized)
- Sample:
- 256 valid captured answers per market edition
- Updated:
- September 2026
Market benchmark
Jersey City AI Visibility Report
A single-market benchmark comparing verified production against how often each team was recommended across repeated captured answers, with the cited sources behind those answers.
- Market:
- Jersey City, NJ · residential
- Sample:
- 256 answers maximum
- Updated:
- September 2026
Source analysis
AI Citation Sources in Real Estate
Which classes of sources appear most often around AI recommendations in a measured real estate market — reported as observed association, not causal proof.
- Market:
- Jersey City, NJ · residential
- Sample:
- 256 answers maximum
- Updated:
- September 2026
Reference
Real Estate AI Search Statistics
Third-party statistics on AI use in the homebuying journey, each attributed to its original publisher with what was actually measured.
- Market:
- United States
- Sample:
- Stated per statistic where published
- Updated:
- September 2026
Research in progress
- Real Estate AI Visibility Index — first edition pending.
06Request a market analysis
One client per market. Start by telling me where you work.
Who This Is For
Built for teams with something worth discovering.
Good fit
- Established real estate teams
- High-producing agents
- Market leaders in their city
- Specialists with a strong transaction record
- Teams with a measurable authority/recommendation gap
- Teams investing in long-term brand authority
This works best for established teams and agents with real expertise, real results and a reputation worth representing accurately.
Not built for
- Guaranteed rankings
- Mass-generated content
- Shortcuts designed to manipulate AI systems
Probably not a fit if you’re looking for guaranteed rankings, mass-generated content or shortcuts designed to manipulate AI systems.
Market Exclusivity
One client. One market.
We don’t retain directly competing teams in the same defined market. Your competitive intelligence stays yours.
Exclusivity defined by
- geography
- service category
- client segment
- active engagement
- agreed contract terms
Market definitions and exclusivity are established during qualification and reflected in the engagement agreement.
FAQ
Questions sophisticated teams ask.
Keep reading
The evidence behind everything on this page.
Request a market analysis
See who AI recommends in your market — and where you stand.
Where you appear, who is recommended instead, and which gaps are worth investigating in your market.
- How often AI names you, and where you rank
- The competitors AI names instead
- The questions you win and the ones you lose
- The websites AI keeps quoting
- What you're missing, ranked by what's worth fixing
A short note reaches me directly. The form on the right requests a full analysis.
Evidence first. Recommendations second. Requests are reviewed before analysis — we don’t sell submitted information or use it to manufacture ranking claims.