A brand-fit scoring layer that reads the foundry's output

Most brand-fit failures fall into four recurring buckets — wrong color language, wrong material language, wrong talent continuity, wrong composition grammar. Each of these is something a human reviewer catches in under a second, which means a well-trained scoring layer can catch it too.

The training signal is not aesthetic preference. It is the brand guide plus a few hundred labeled approvals and rejections. That is enough for the scoring layer to agree with a senior art director on most calls — once it is trained, it scales across every campaign.

We place the scoring layer immediately downstream of generation. Every candidate surface gets scored, low-scoring output is filtered out, and the surviving set goes to the art director for curation rather than rejection.

The net effect is that review moves from checking every surface to curating a pre-filtered survivor set. Throughput goes up by five to ten times on repeat campaigns without loss of quality.