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Platform · Governance

Control, by design.

Product data is strategic, and in many categories it is regulated. Our job is to make sure the output is always controlled, never unmanaged. Your rules become the standard, every output is scored against it, and a human keeps the final say.

The catalog grid on the online store dataset: eighteen products, values the agent flagged in amber, missing values in pink, and the issue on a short description read where the value is.

North Star rules

Every agent is held to the same standard, and that standard is your rules.

Your rules - from brand voice to category and compliance requirements - become instructions every agent follows automatically.

Your rules, applied automatically.
Brand voice, content guidelines, category requirements and compliance rules - encoded once and applied every time.
One standard, everywhere.
Set once, applied across every SKU, market, and channel, scoped per market and category where needed.
What the agent read and what it checked on one value: the supplier feed and the brand data sheet both had something, web search did not, and of the four checks it ran three passed and one failed on technical materials.

Automated checks, human judgment

Your team stays in control, without checking every product.

Every output is scored against your rules before it moves. What the system is not sure about is flagged and waits for a person; what passes with confidence publishes.

Every output scored before it moves.
Format and schema checks catch the hard errors, an AI grader reads the rest against your rules, and the scores track quality across the whole catalog.
Review only what needs your attention.
Issues are flagged automatically, so your team spends its judgment on the exceptions instead of re-reading the catalog.
The review panel beside the grid on one attribute: the current short description, the rule it breaks, the suggested revision, apply or keep, and the related attributes the agent read to decide.

What control looks like

Measured, not assumed

100%
of outputs checked against your rules
Continuous
quality tracking, not a one-off
0
unmanaged outputs reach your catalog

Why it matters

Control every output. Build trust, stay compliant, and publish with confidence.

  1. 01

    Trust

    Every output is checked against your standards before it ships, so your team can trust AI with more of the work.

  2. 02

    Compliance

    Regulated claims and category rules are applied automatically, with a full audit trail behind every decision.

  3. 03

    A compounding advantage

    Your team's decisions become part of the standard, so quality improves over time.

Questions

Questions we get asked

What counts as a rule?
Anything you would send a product page back for. How you sound, the claims you refuse, what a category is required to state, the format a value has to take, the target a market is held to. You write it down once, and from then on every agent answers to it.
How do you test quality?
In layers, cheapest test first. Format and schema are settled outright, because a malformed value needs no judgment. What is left goes to a grader that reads it against your own rubric, and those scores roll up across the catalog so no category hides behind an average. Where the two disagree, a person decides, and that decision becomes the reference. Nothing moves without a score.
Do you check every output?
Yes. Every output runs through the quality checks against your rules, continuously, not sampled once at launch.
How do we stay in control of the output?
Every output is checked against your rules before it can publish. What the system is not sure about is flagged and waits for a person. What passes with confidence goes out, so your team is not re-reading a catalog to find the few values that need a decision.

Bring the checks you cannot get wrong

Bring a category, its rules, and the products you would use to judge it. We build the set with your team.