# Control, by design. *Platform · Governance* > Governance is the CommerceClarity platform surface that keeps the output controlled rather than unmanaged. The customer’s own rules become the standard every agent is held to, encoded once and applied across every SKU, market and channel. Each agent carries a KPI, a measurable quality target set from those rules and scored on every output before it moves, with layered checks that catch regressions and drift. Values are checked against the rules across the whole catalog rather than a sample, and a person approves everything that publishes, working from a review queue that is already scored. 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. [Book a demo](/book-a-demo) ![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.](https://cdn.sanity.io/images/xd4hrbt2/production/d28affc652f461d768e312804e68de5ce65dead5-2880x1920.webp) ## Blocks ### Every agent is held to the same standard, and that standard is your rules. standard *North Star 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. **Alt** 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. **Url** [https://cdn.sanity.io/images/xd4hrbt2/production/763b8520034a300ff2cea7f91f83e47fc6cfeab5-1600x976.png](https://cdn.sanity.io/images/xd4hrbt2/production/763b8520034a300ff2cea7f91f83e47fc6cfeab5-1600x976.png) ### From ‘it feels right’ to ‘it scores on the cases that matter.’ measured *Automated quality checks* 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. **Alt:** The two scores a catalog is measured on: completeness at 80 per cent, marked good, and accuracy at 95 per cent. **Url** [https://cdn.sanity.io/images/xd4hrbt2/production/2c0fd6b0de1c6e9f7612e3a3efd2e180f528466e-1698x270.png](https://cdn.sanity.io/images/xd4hrbt2/production/2c0fd6b0de1c6e9f7612e3a3efd2e180f528466e-1698x270.png) **Alt** 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. **Url** [https://cdn.sanity.io/images/xd4hrbt2/production/2b51eecc63a298e2c23118c50739392041ac1792-1698x506.png](https://cdn.sanity.io/images/xd4hrbt2/production/2b51eecc63a298e2c23118c50739392041ac1792-1698x506.png) ### A person approves everything. The scores are what make that possible. production *Human in the loop, at scale* 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. **Alt** 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. **Url** [https://cdn.sanity.io/images/xd4hrbt2/production/4f126f984186173c87085870047cf04b4ad6bb64-1538x1140.webp](https://cdn.sanity.io/images/xd4hrbt2/production/4f126f984186173c87085870047cf04b4ad6bb64-1538x1140.webp) ## Measured, not assumed *What control looks like* - 100% · of outputs checked against your rules - Continuous · ∞ · quality tracking, not a one-off - 0 · unmanaged outputs reach your catalog ## Control on the output means trust, compliance, and a catalog you can stand behind. *Why it matters* - Trust · Every output is checked against your standard before it ships, so you can put AI in front of the business. - Compliance · Regulated claims and category rules applied automatically, with a full audit trail behind them. - A compounding advantage · Every correction feeds back, turning your feedback into an edge on your own catalog. ## 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. [Book a demo](/book-a-demo) ## Questions ### 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. ## Entities - [EU Artificial Intelligence Act](https://eur-lex.europa.eu/eli/reg/2024/1689/oj) · Thing, mentions ## Sources - [AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) · NIST - [Regulation (EU) 2024/1689, the Artificial Intelligence Act](https://eur-lex.europa.eu/eli/reg/2024/1689/oj) · EUR-Lex - [Regulation (EU) 2023/988 on general product safety](https://eur-lex.europa.eu/eli/reg/2023/988/oj) · EUR-Lex - [Creating helpful, reliable, people-first content](https://developers.google.com/search/docs/fundamentals/creating-helpful-content) · Google Search Central --- Canonical: https://commerceclarity.com/platform/governance Every page of this site is available as markdown: append `.md` to its path. Index: /llms.txt