AI use case

Content Generation from Product Data

Transform your product data into clear, consistent content ready to be distributed across all your channels.

  • Executive
  • Marketing
  • Sales
Product data rarely arrives as text ready to publish. It is a nomenclature export, a technical datasheet a supplier sent as a PDF, a table of specifications someone has been copying by hand into the CMS for three years. Fasfox builds systems that read these sources as they actually exist and draft the content each channel expects, working from fields that are already validated rather than reinventing them.

The product sheet before it is a product sheet

Behind every published sheet sits an article reference, an internal code, a table of dimensions in millimetres, and a technical datasheet the supplier updated without telling anyone. A content coordinator copies these into the PIM before each release, reference by reference, across a catalogue that can run to several thousand items, checking each field against the supplier document open in another window.

Fluent text can still be wrong

A model writes a smooth description even when a value is off: a variant discontinued last year resurfaces, a diameter is misread from a scanned table, a regulatory characteristic (fire rating, ingress protection) gets rephrased without its exact meaning being checked. Demonstrations never show this, because they run on records that are already clean. In production, it is precisely the messy records that cause trouble.

A maintained source, or the project waits

Before generating anything, there needs to be a single source where fields carry stable names across references, and a named person to arbitrate when two sources disagree. If specifications live scattered across PDFs with no shared structure, the base needs cleaning before writing gets automated: producing text from uncertain data only pushes the verification work further down the chain.

Draft at volume, publish after a targeted check

The system we build drafts descriptions and their channel variants from validated fields, and flags cases where data is missing or looks inconsistent rather than publishing them as is. Channels with regulatory or safety implications keep a human check before release; others can publish under a lighter review. Where that line sits is decided channel by channel, not sheet by sheet.