Martenfield

Services

A small first step, published prices.

Each retainer contracts on the metric its work moves, across a named query set over 90 days, measured identically both times, under method v1.1: citation share on the first tier, recommendation share on the second. Both are reported on both. Prices are published so the anchoring happens before the call rather than during it.

01 · Diagnosis

$1,200

2 weeks · one-time

One category. Your prompts, frozen before we run them. We measure where the engines currently place you, what they cite when they answer, and which of your competitors they name instead.

Proptech and CRE software companies who suspect they are absent from AI answers and want the size of the problem, dated, before committing to anything else.

  • 50 buyer-intent queries across 5 engines at 3 runs each, frozen and dated before the first run
  • The raw runs: one row per run, per query, per engine, with every cited URL
  • The method version that produced them
  • Source map: which domains the models cite for your category
  • and 4 more

What this will not do

Tells you where you stand and how much the engines disagree with themselves across runs. Changes nothing on its own.

Full scope →

02 · Retainer

Retainer, tier 1 · the default

$2,500 a month

3-month minimum · per month

First 90 days $8,700

Resolution and citation. Entity work, schema, and making your own data readable and citable, with the build done inside the first 3 months and measurement throughout.

Companies holding proprietary data that their buyers ask about and their market cannot currently read. This is where we recommend starting after a diagnosis, and it is the default retainer.

  • The build, done inside the first 3 months, with no separate fee
  • Entity architecture: the company, its products and its named executives resolved and cross-linked
  • Data publishing pipeline from your existing database, with source-grade assets built from data you already hold
  • Weekly tracking of the same 50 queries across 5 engines, re-measured at 90 days
  • and 5 more

What this will not do

Fixes how engines describe you and makes your own data citable. Will not get you named as a vendor where you have no third-party mentions. That is the boundary, and it is why the retainer with earned mentions exists.

Full scope →

03 · Retainer with earned mentions

Retainer, tier 2 · adds earned mentions

$4,000 a month

3-month minimum · per month

First 90 days $13,200

Everything in the $2,500 retainer, plus earned-mention work: third-party source acquisition, comparison-layer placement and outreach. This is the only ongoing work that moves whether an engine names you as a vendor.

Companies the engines already place and describe correctly, who are still missing from the vendor shortlist because too little outside their own domain discusses them.

  • Everything in the $2,500 retainer, the build included
  • Third-party source acquisition, targeted from the source map
  • Comparison-layer placement: presence on the pages that compare vendors in your category
  • Outreach, pitched with original data from your own assets
  • and 2 more

What this will not do

Recommendation moves in quarters, not weeks, and it depends on sources we do not control. Nobody can promise a naming rate.

Full scope →

Both 90-day totals include the diagnosis. The build is done inside the first 3 months of either retainer and is never a separate line item. Citation and recommendation are measured and reported separately on both tiers, and never combined into one visibility number.

Why the first commitment is small

01 The diagnosis is a preview of the work
Same instrument, same method, run on your own category. You see the output before committing to anything, and if you never work with us again you keep the baseline.
02 3-month minimum
Short enough to run alongside an incumbent agency rather than requiring a switch.
03 Build included
The build is done inside the first 3 months of either retainer. There is no five-figure second door after the small first one.
04 Prices published
No quote process, and no discovery call required to learn the number.
05 Measurement is the same at both retainer prices
Nobody gets a shallower read for paying less. The higher tier adds work, and the view of that work is identical.

Client results

What we don't promise

  • Pipeline numbers from AI answers
  • Demo counts attributed to a model
  • Attributed revenue from generated answers

Attribution from generated answers is unreliable in 2026. We contract on a leading indicator, not the invoice: citation share on the first retainer tier and recommendation share on the second.

Recommendation share is the proportion of answers, across a defined query set, in which a company is named as a vendor to consider. Citation share is the proportion of answers in which a URL on a domain the company owns appears as a cited source. They are reported separately and never merged.

Who we turn down

  • Individual agents and local brokerages
  • No proprietary data to publish
  • Budget under $2,500 a month after the diagnosis
  • First question is the monthly rate

A written disqualify list is cheaper than a bad engagement, for both sides.

The questions a model tells buyers to ask us

Asked how to evaluate a GEO firm, ChatGPT gives buyers essentially this list. Here are our answers, including the two that are hardest to give.

Can you show before and after AI visibility across multiple engines?
Not for a client yet. No client re-measurement has happened, and our results page stays empty until one does. When one exists it will be reported per engine across all 5, rather than as one blended figure. What we can show today is our own property measured with the same instrument: the REN.PH evidence page publishes both console exports and states at the top what it does not demonstrate.
Which specific prompts do you track, and who chose them?
You approve them. We draft 50 buyer-intent queries from your category, your competitors and the questions your sales team already answers, and you sign off the set before anything runs. It is frozen and dated at that point, so it cannot be adjusted afterwards to flatter a result, and you hold a copy.
How do you measure share of AI answers rather than Google rankings?
Two numbers, measured identically before and after. Recommendation share is the proportion of answers, across a defined query set, in which a company is named as a vendor to consider. Citation share is the proportion of answers in which a URL on a domain the company owns appears as a cited source. They move independently and they are fixed by different work. 50 buyer-intent queries you approve first, across 5 engines, 3 runs each, logged out, with raw responses stored rather than parsed results. Rankings are not part of it and neither is organic traffic. The method is published in full and versioned.
What share of the work is technical and entity work, content, and off-site authority?
It depends on the tier, and the split is the difference between them. On the $2,500 a month retainer the work is technical, entity and data publishing, front-loaded into the build, with no off-site work. The $4,000 a month retainer adds off-site work: third-party sources, comparison pages and outreach. We have not run enough engagements to state a percentage split, and we are not going to estimate one.
Which third-party sources are earning the mentions?
For a client, the ones the source map shows the engines citing in that category, which differ by category and change over time. The monthly report names them by domain. We have no list of earned mentions to show you yet, because no earned-mention engagement has completed a quarter.
Have you worked in this specific vertical rather than generic SEO?
The vertical experience is real estate data infrastructure we built and operate. In June 2026 ChatGPT pointed a US commercial real estate buyer at a service brand we run and they reached us from there, which is one enquiry traced by asking the buyer, and our About page states what it does not prove. We have no proptech clients yet. There is no roster of proptech SaaS clients, and if a firm implies one at this stage of this market you should ask which of them will take your call.
Does the method reproduce without a proprietary dataset behind it?
Yes, and that is a design requirement. The method is published and versioned, the query set is frozen before collection, and the raw responses are stored. Anyone with access to the same engines can run the same set and get their own numbers. They will not get identical text, because generated answers vary between runs, which is why the method takes 3 runs and reports what the engines tend to say. The one part not yet published is the crawler registry, and our About page says so.
What stops a client building this in-house?
Nothing. The method is published so that you could, and measurement is the cheap part. What you would be taking on is the weekly tracking, the judgement about where the source map moved, and on the higher tier the outreach, which is the least automatable work in this stack. If you have someone who can own that, build it, and use a diagnosis as the dated baseline.