Why we exist
Buyers changed how they ask. Agencies did not change what they sell.
Most agencies sell optimization before measuring anything: a framework, an audit of your website, a package with GEO in the name, and no instrument that says whether assistants recommend you this month. When someone asks ChatGPT, Gemini or Perplexity about your category, the answer names three to five brands, and most of the market has no way to know who made that list or why.
Google’s own guide settles what the optimization half is: “optimizing for generative AI search is still SEO” (Search Central, “Optimizing for generative AI search”, updated July 2026: https://developers.google.com/search/docs/fundamentals/ai-optimization-guide). The same guide names llms.txt, chunking and special schema as myths. We agree with all of it. That agreement is our starting point, not our product.
What is genuinely new is measurement: whether assistants recommend you, against whom you compete inside the answers, and which sources they trust. That did not exist in traditional SEO tooling, so we built it.
What we believe
Four beliefs, each with a consequence for how we work.
Evidence over hacks. If Google’s documentation says something does not work, we do not sell it, whatever the margin. If a claim in our reports cannot be traced to a response, it does not ship.
The method is published. How the prompt bank is built, which models run, how many repetitions, what gets counted, and what the method cannot do: /metodologia/. You can ask to see raw output from any study, including ours.
Rates, not scores. Every figure is a rate with its denominator printed next to it. A percentage with no population behind it is a decoration. There is no position one in AI answers, no composite score, and no magic number; whoever sells you one invented it.
Honest limits. A comparison of two runs does not prove causation. Third parties write most of what gets cited. The first run is a baseline, not a fix. And if nothing moved, the report says nothing moved.
We commit to two things: a prompt bank that is complete and useful for your brand, whatever your industry, and a run that produces useful findings. What nobody can guarantee — us included — is that the actions you take afterwards will move the numbers in a later measurement. The method is public precisely so you can judge the findings, not the promise.
Our relationship to answer.show
We built the measurement tool. answer.show is what runs the studies: the prompt bank, the models, the repetitions, the reading and coding of every response. It is the reason we can show raw data, repeat a measurement on demand, and prove every claim in a report.
The agency is the done-for-you layer around that tool. We run the levantamiento, build and freeze your bank, read the responses, and hand your team priorities, not another dashboard. And where the tool stops, the agency continues: answer.show measures, and the agency also executes the work the report points at — the retailer and marketplace pages, the commissioned editorial, the earned media and reviews it names. That service is the work on the sources.
If your team would rather run the measurement itself, answer.show has self-serve tooling. We would rather you measure well than buy a service you do not need.
Who we work with
Consumer brands. Your buyer walks: assistant question, retailer app, reviews, shelf, with zero visits to your site. The sources that decide the answer are retailer pages, reviews, consumer media and regulators, and most of them sit outside your control. The study tells you which ones move it and what to do about it.
B2B and SaaS. Considered purchases with long cycles, where buying committees ask assistants early, in evaluation questions you never see. The study shows who gets named instead of you, and which sources put them there.
Agencies. Wholesale runs, white-label reports, and prompt bank commissions, with no tooling to build: /partners/.
And anyone who needs the instrument more than the service. If self-serve fits you better, we will say so.
Who is behind this
reasoning.agency is run by Pablo Castro. He studied Computer Systems Engineering at ITESM and did an MBA at London Business School, then spent more than ten years at Meta, Google and Apple: leading sales and alliance strategy for Latin American markets at Meta, managing strategic partnerships with key advertisers and agencies at Google, and covering the partner ecosystem as a channel account executive at Apple.
What those years taught him is the thesis this agency runs on: technology adoption is an organizational problem, not a technical one. What decides whether a tool gets used is a team’s real workflows, not demos nobody uses afterward. Measurement first is the same idea applied to AI visibility: before optimizing anything, find out what is actually happening. He also runs academIA, where he offers consulting and training for AI adoption inside companies.
Work with us
The study is the entry point: a conversation about the decision you need to make, then the levantamiento, then a scope agreed and approved before anything runs.
Contact: /contacto/