Model Quality

How Solid evaluates, maintains, and improves semantic model accuracy over time — through benchmarking, auto-maintenance, and drift detection.

A semantic model is only useful if it stays accurate as your data evolves. Model Quality covers the systems Solid uses to measure accuracy (benchmarking), detect when the model has drifted from the underlying data, and automatically surface fixes — so model quality is an ongoing property, not a one-time setup.

Updated 11 days ago