21. What results have teams seen?

Engagements under Breeze.AI report these results, each in its own context. On one engagement across three products, deployments rose from 19 to 36 a month over a fourteen-week pilot. On the same engagement, lead time for changes fell from 2.0 to 1.42 days. Extracting the graph from a codebase of more than two million lines took two to three weeks. On one brownfield application, impact analysis replaced three to five days of investigation by a senior engineer before each change. In the first sprint of a new user-interface workstream, 53% of design components were reused. Defects fell 23% compared with the same team's results before Semantic Engineering, on the same codebase. Test coverage reached 93.4%, with test scenarios generated from the functional layer and no manual scenario writing. A cost model puts five-year total cost 81% lower when the AI runs on the client's own infrastructure; the figure comes from the model and was not measured on an engagement.

On the site: Numbers from Real Engagements · Case Archetypes: Continuous SDLC