Evaluating a Decision Model on Your Own Content, Not the Vendor's Benchmark
A vendor benchmark tells you a model agreed with two frontier models 67.8% of the time.
Browse by topic
A vendor benchmark tells you a model agreed with two frontier models 67.8% of the time.
Ship a 5,000-page catalog and the accessibility audit comes back with the same finding every quarter: thousands of images with empty alt attributes, product pages with no meta descriptions, and article summaries that were never written…
When a marketing team ships AI-generated headline variants without a testing loop, they are guessing.
When a retrieval-augmented feature times out in production, the failure is rarely loud.
A support team ships a macro that quietly goes stale.
A marketing team ships a landing page at 4:58pm on a Friday.
You inherit a site with 40,000 pages, and half of them are missing meta descriptions, alt text, or Open Graph tags.
Most headline A/B tests die in a spreadsheet. A growth marketer writes four variants in a Google Doc, pastes them into a feature-flag tool, wires up an analytics event, and then waits two weeks for a result that the CMS never learns from.
Your team shipped an AI writing assistant for the CMS last quarter. It generated a product description, an editor accepted it, and three weeks later support flagged that the spec it cited was for last year's model.
A content team ships a product launch page, and three hours later the AI-generated FAQ block at the bottom is quietly wrong: it cites a price tier that was deprecated last quarter. Nobody approved that copy.
Your editor publishes a product page in French, and forty minutes later support is fielding tickets because the German, Japanese, and Spanish versions never got translated, the page shipped without alt text, and a competitor's name slipped…