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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 company rules (what to say, what not to say, brand voice, content and category rules) become rules the agents follow automatically on every output, not left to chance.

Behavioral standards from your rules
Brand voice, content do’s and don’ts, category and compliance, 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 quality checks

From ‘it feels right’ to ‘it scores on the cases that matter.’

Every agent carries a KPI: a measurable quality target set from your rules, scored on every output before it moves. The checks catch when a change quietly degrades quality, and anything that drifts from your rules.

A layered approach
Exact checks on format and schema, a model that grades against your rubric, quality scores across the catalog, and human judgment as the gold standard.
Catches regressions and drift
A quiet quality drop from a model or prompt change is flagged before publish, not shipped.
The two scores a catalog is measured on: completeness at 80 per cent, marked good, and accuracy at 95 per cent.A proposed rule change on the question and answer attribute, marked high impact, covering the four questions buyers ask AI assistants, with the products it would affect.

Human in the loop, at scale

A person approves everything. The scores are what make that possible.

You see exactly which values do not respect your rules, attribute by attribute, across the whole catalog rather than a sample. So your team arrives at a queue that is already scored and already ranked, and confirms a judgment instead of forming one from scratch on every record.

Scored before anyone opens it
Every record reaches review already graded against your rules, with the failures marked in the cell, so attention goes where it is needed.
A person approves everything that publishes
No value reaches a channel on the agent's word alone, and every published value carries the rule it passed.
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 on the output means trust, compliance, and a catalog you can stand behind.

  1. 01

    Trust

    Every output is checked against your standard before it ships, so you can put AI in front of the business.

  2. 02

    Compliance

    Regulated claims and category rules applied automatically, with a full audit trail behind them.

  3. 03

    A compounding advantage

    Every correction feeds back, turning your feedback into an edge on your own catalog.

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.
Does a human approve everything?
Yes. A person approves everything that publishes. The automated checks do not replace that approval, they make it possible at catalog scale: every value arrives already scored against your rules, with anything uncertain marked.

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.