Solutions · Agentic Commerce
Make products readable by shopping agents.
Agents that publish your catalog where autonomous shopping agents can read it and buy from it: OpenAI’s product spec for ChatGPT, Google Merchant with the conversational attributes its AI surfaces consume, and whatever surface comes after them.
The problem
A new buyer, with no eyes
A shopping agent does not browse. It reads a feed, compares structured attributes and completes a purchase, and it never sees the page you designed. Everything built to persuade a person, the photography, the layout, the copy above the fold, is invisible to it.
What it does read is a specification, and the requirements are somebody else’s. OpenAI publishes a product spec with its own attribute set and its own update cadence. Google wants the classic Shopping feed and, separately, the conversational attributes its AI surfaces consume. Neither one is your PIM’s schema.
This is also the channel where being early is cheap and being late is not. The feed either validates and republishes on cadence or your products are not in the answer, and there is no ranking to slip slowly down: you are in the consideration set, or you are not in it at all.
Common use cases
The jobs on the feed
A few of the ones that run here, and the list grows every time a surface does. Each is a single workflow with its own output and its own numbers. Publish to one surface, or to all of them.
Publishing to the agents
One pipeline, every surface that reads a feed
For Google, both layers run on one pipeline. The classic mapping onto its required attributes outputs a feed that passes validation for Shopping ads and free listings. The conversational attributes are the second layer, the ones its AI surfaces read in order to recommend a product and complete a purchase. For ChatGPT, the same catalog is transformed to OpenAI’s product spec, its attribute set and its update cadence, then validated and republished on that cadence. Both run off the same source record, so the feed that ranks and the feed that gets recommended stay in step. And when a spec moves, which these specs are young enough to keep doing, we maintain the mapping.
Ask the catalog
AgentAnswers carry their sources
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
How is this different from the Google feed we already have?
The classic layer is the feed you know, maintained rather than set up once. The conversational layer is new: a separate attribute set that Google’s AI surfaces read, and a feed built only for Shopping ads does not carry it.
Is ChatGPT shopping worth a workflow today?
That is your call on timing, and it is a cheap one to take early. It is the same product knowledge mapped onto a published spec, so the work is the mapping and the cadence rather than a new content project.
What happens when the spec changes?
We maintain the mapping. These specs are young and they move, which is the argument for running this continuously instead of exporting once and calling it done.
Do we lose control of how we are presented?
You control the input, which is more than it sounds: the attributes and the answers you publish are what the agent reasons on. What you do not control is the comparison, and that is true of any marketplace.
Does this replace SEO?
No. It is a different surface with its own feed and its own requirements. The overlap is the product knowledge underneath, which is why these areas share it.
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.