Solutions · SEO & GEO
Get products found by search engines and AI.
Agents working the same product knowledge for both demand surfaces: the words shoppers actually type, the content the AI engines lift into an answer, and a question block on every page. At a cost per product low enough to reach the tail, which is where the unclaimed demand is.
The problem
Your catalog does not speak the way shoppers ask
A product page is usually written the way the business files the product: the supplier’s name for it, the specification in the manufacturer’s words, a description written once for a brochure. Shoppers do not phrase it that way, and neither do the engines that increasingly answer on their behalf.
There are two demand surfaces now and they reward different things. Google still reads a title and a description. An AI engine lifts whatever is structured enough to quote: a direct answer, a comparison, the trade-off, the question somebody actually asked. A catalog optimized for the first is not optimized for the second.
What makes this economic is the tail. Rewriting the top hundred products is a morning of work for one person, and it is also the part that already ranks. The rest of the catalog is where the demand is unclaimed, and it only ever gets written if the cost per product is close to zero.
Common use cases
The jobs on demand
A few of the ones that run here, and the list grows as the surfaces do. Each is a single workflow with its own output and its own numbers, working off the same product knowledge underneath. Run one, or run the set.
Capturing demand
Written for the list, and for the answer
The same product knowledge, worked for two surfaces. For search: how people actually phrase a query in your categories, on Google and inside marketplace search, with titles and descriptions rewritten against those phrasings rather than against the supplier’s name for the product. For the answer engines: the comparisons, trade-offs and direct answers in the shapes an engine will lift. A question block on every page earns on both surfaces, built from the questions your pages never answered: Google reads it for People Also Ask, and an AI engine quotes it when it recommends the product. Underneath both sits the measurement, rankings on one side and citation share on the other. All of it runs per product, which is the only reason it reaches past the hundred products somebody already optimized by hand.
Keywords · Footwear
ClimbingPosition · 30-day move · volume
The feedback loop
Every correction makes the next run better
Your team judges the output. A value is right, or it is wrong and gets fixed once. Each verdict changes the next run: the score on your own sample moves, and the agent starts from what the last one learned. So the share of values that need a person keeps falling, measured on your catalog and your rules rather than on an average of everybody else.
How we work
Live on your catalog in weeks, not months
We run all four phases with your team, on the stack you already have, and each one ends with a number you agreed in advance.
- 01
Design
We map how your data comes in and how the result goes back out, then agree the business KPI the agent has to move.
- 02
Implement
We build the agent and test it on a sample of your own catalog, agreed with you. Every change is scored against that sample, so the tuning runs on numbers.
- 03
Prove
Done means the KPI reached target on the full catalog, and you have seen the numbers yourself.
- 04
Evolve
We stay on the account. Your corrections keep improving the agent, the KPI holds where you need it, and the next use case starts from the context this one already built.
Enterprise, on a catalog you cannot break
It runs on the stack you have
It reads your PIM, ERP and DAM through their APIs and writes the result back to them.
A person approves everything that publishes
Every value is checked against your rules, and anything uncertain goes to review before it reaches a channel.
Your data stays yours
Your own isolated workspace. It is never pooled into shared models, and you can export it at any point.
Every run is logged
Each value carries its source, its run and its score, so you can see why a wrong one went wrong.
Questions
What retailers ask us
Is GEO just SEO with a new name?
No. Ranking a page and being quoted in an answer are different mechanics. SEO optimizes a title and a description for a list of results. GEO structures the content an engine will lift, and the format that gets lifted is not the format that ranks.
Do we have to rewrite the whole catalog?
No. The work goes where the demand is unclaimed, which is usually the tail rather than the products somebody already optimized by hand.
How is it measured?
On the demand surface rather than on the output. Rankings and traffic for the search side, citation share for the answer side. Tracking that citation share is its own workflow, separate from the writing.
Will the content read like a machine wrote it?
It is written against your own rules: tone of voice, glossary, the claims you are allowed to make. Anything the rules do not cover is held for review instead of published.
Does a generated question block clash with our real customer questions?
No, it fills the gap ahead of them. Where you already have customer questions and answers, those stay. The agent covers the pages where nobody has asked anything yet.
Whose data is it?
Yours. The corrections your team makes are what the agents learn from, and both the data and the corrections stay exportable at any point.