The call grade, management's tone measured against 177,437 other calls, what changed since the quarter before, and every one of the 500 checks.
This is the current report. Unlock its call read, expected move size, and all 500 answers.
Language counted straight from the transcript, behaviour read by the 500 checks, tone above. The band is the middle half of every call we have graded; a flag means this call sits outside it.
Q1 2026 · 500 checks
Every answer is below. Open any check to see what its YES or NO means.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model found enough evidence in the earnings transcript to mark this exact condition as present.
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
The model did not find enough evidence in the earnings transcript to confirm this exact condition. NO means “not detected,” not necessarily “disproved.”
Earlier Artul research experiments on RTX calls. Open a question to read the model's original reasoning.
Original model output from the research archive. It may be verbose or include the model's working notes.
The task is to evaluate two gates based on the earnings call transcript and return YES only if both are YES, otherwise NO. YES The transcript shows clear positive current momentum in GTF fleet management and MRO ramp-up, with management presenting it as on track and improving.
We need to evaluate both gates based on the transcript. Gate 1: Is there a positive, current development that management presents as going well, and does the Q&A produce more concrete operating detail than the prepared remarks? The prepared remarks highlight commercial aftermarket strength, recovery, etc. In Q&A, analysts probe various topics. Let's see if any positive development gets more detail. Analysts ask about supply chain, defense, narrow-body, etc. The positive development could be commercial aftermarket recovery. But does Q&A add concrete detail? For example, on aftermarket, management mentions provisioning up 73%, parts and repair up 43%, etc. In Q&A, they discuss China impact, but that's more about risks. They also discuss pricing, but that's not necessarily a current development. Another positive: defense budgets and potential upside. But that's future. The question asks: "is there a POSITIVE, CURRENT development... for which the question-and-answer discussion PRODUCES MORE CONCRETE OPERATING DETAIL THAN THE PREPARED REMARKS CONTAINED" and "management's answers keep ADDING new, specific, present-tense substance." Looking at the Q&A, analysts ask about supply chain constraints, which is a problem, not a positive. They ask about narrow-body build rates, but management talks about challenges. They ask about aftermarket, but management gives some detail but not much new. The prepared remarks already gave a lot of detail on aftermarket growth. In Q&A, when asked about aftermarket, management talks about China impact and pricing, but not necessarily adding new concrete operating detail about the positive development. Perhaps the positive development is the commercial aftermarket recovery. But the Q&A doesn't seem to add much new specific substance beyond what was in the prepared remarks. For example, when asked about aftermarket, Greg says "we remain optimistic" and talks about China, but doesn't give new specifics like new customers, volumes, etc. Also, the Q&A often focuses on problems like supply chain, Russia, etc. Thus, Gate 1 likely NO. Gate 2: Improvement filter. Even if there were a positive development, is it company-created or external? The commercial aftermarket recovery is driven by external factors like air travel recovery, not company-specific. So even if Gate 1 were yes, Gate 2 would be no. Thus, answer NO.
Original model output from the research archive. It may be verbose or include the model's working notes.
We need to determine if there is a positive, current development in the company's business that is discussed in the Q&A with more concrete operating detail than in the prepared remarks. The question asks for a YES/NO answer. First, identify positive current developments mentioned in the prepared remarks. The prepared remarks mention: commercial aftermarket growth (38% in Q1), defense backlog strong, Russia impact, supply chain constraints, etc. But the question asks for a positive development that is probed in Q&A and yields more concrete detail. Look at the Q&A exchanges. The first question from Myles Walton asks about supply chain constraints, specifically Pratt commercial engines and rocket motors. Greg Hayes responds with details about structural casting supplier, 70 engines moved out of Q1, recovery plan, rocket motor issues. That's about problems, not a positive development. Second question from Robert Spingarn about defense demand, specifically Stinger replenishment. Greg Hayes talks about production, material, redesign, timing. That's about future opportunities, not current positive development. Third question from Peter Arment about narrow-body build rate. Greg Hayes discusses supply chain issues, titanium, etc. Not a positive current development. Fourth question from Robert Stallard about Russia impact on revenues. That's about negative impact. Fifth question from Ron Epstein about titanium sourcing. That's about problems. Sixth question from Sheila Kahyaoglu about profitability and portfolio. Neil Mitchill talks about strong margins at Collins, aftermarket provisioning up 73%. That is a positive current development. But does the Q&A add more concrete detail than prepared remarks? The prepared remarks already mentioned commercial aftermarket up 38% at Collins, provisioning up 73% in the segment results. In the Q&A, Neil says "very strong margins for Collins in the quarter, getting a little bit of feedback there. So good margins. The aftermarket, in particular, at Collins, performed quite well with provisioning up 73% year-over-year, and that was really what drove the favorability in the first quarter for us." That is essentially repeating the prepared remarks. Then he talks about expectations for the rest of the year, but that's not new current detail. So that exchange doesn't add much. Seventh question from David Strauss about Boeing MAX and 787.
Original model output from the research archive. It may be verbose or include the model's working notes.
We need answer only YES or NO. Need evaluate both gates using transcript. We need determine if there is positive current development with Q&A adding concrete operating detail beyond prepared remarks, and broad demand base. Let's analyze transcript. Prepared remarks: commercial aftermarket strong, 38% growth, Collins aftermarket up 39%, provisioning up 73%, parts repair 43%, mods 11%. Defense backlog strong. Q&A probes: aftermarket, supply chain, Russia, defense budgets, GTF castings, China, etc. Gate 1: Is there a positive current development where Q&A produces more concrete operating detail than prepared remarks? Candidate: commercial aftermarket recovery. Prepared remarks already gave growth percentages. Q&A: Myles asks supply chain constraints, not aftermarket. Robert asks defense. Peter asks narrow-body build rate. Greg gives supply chain details. Robert Stallard asks Russia impact. Ron asks titanium. Sheila asks profitability. David asks Boeing rates. Kristine asks castings/inflation. Doug asks defense trajectory. Noah asks margins. Mike asks GTF. Ken asks China. Seth asks Airbus rates. Cai asks price hike. Is there a positive current development probed with additive detail? Maybe commercial aftermarket? In Q&A, not much additional aftermarket detail beyond prepared. There is discussion of aftermarket in answers to Sheila about margins: provisioning up 73% drove favorability, but that was in prepared remarks. No new specifics. China aftermarket: Greg says China accounts 16% RPMs, domestic down 80%, expects recovery; Neil says engine center in Shanghai shut down, Pratt aftermarket flat sequentially, Collins low single digit. That's about weakness/risk, not positive development. Defense spending? Prepared remarks mention budget request, backlog. Q&A: Greg says defense sales forecast doesn't include upside from replenishment, stingers production issues, timing 23/24. That's future opportunity, not current development. Also not broad? Defense is concentrated contracts. Commercial aftermarket is positive current, but Q&A doesn't add much concrete operating detail beyond prepared. Maybe Q&A adds: aftermarket price increase 6% catalog, but that's pricing not volume. Also "operators began preparing fleets for summer" in prepared. Q&A: "provisioning up 73%" repeated. No new.
Original model output from the research archive. It may be verbose or include the model's working notes.
We need to determine if there is a specific negative outcome that investors/analysts/market are worried about, and if management pushes back with current, already-observed evidence. Let's analyze the transcript. The call covers Q1 2022 results. Key themes: supply chain constraints, Russia/Ukraine impact, commercial aerospace recovery, defense spending. Analysts ask about supply chain constraints (Myles Walton), Russia impact on revenues (Robert Stallard), titanium situation (Ron Epstein), China travel (Ken Herbert), etc. Management acknowledges supply chain issues, but also says they are mitigating. For example, on the structural casting issue, Greg says "that problem is not behind us, but we are working with that supplier to recover that, and we'll get most of the way there by the end of the year." That is a plan, not evidence of non-materialization. On Russia, they reduced sales outlook by $750M, but held EPS. They say they are offsetting through cost containment. That is not evidence that the feared outcome (loss of Russia business) is not materializing; it is materializing, they are mitigating. On China, they say it will recover, but that is a projection. Is there a specific fear that is live and management counters with observed evidence? For example, analysts might worry about commercial aftermarket slowdown, but management points to strong aftermarket growth (38% in Q1). That is observed evidence. But is that a fear? The fear might be that aftermarket recovery is slowing, but management shows it grew 38% and sequentially up 5%. That is evidence. But the question asks for a specific negative outcome that investors are worried about. Let's see if any analyst explicitly states a fear. Myles Walton asks about supply chain constraints and if they have passed the low point. Management says they are working on it, but not fully recovered. That is not a pushback with evidence. Robert Stallard asks about Russia revenue impact. Management says they are losing sales but offsetting earnings. That is conceding the fear. Ron Epstein asks about titanium. Management says they have inventory for most of the year, but some components will be late. That is conceding. Ken Herbert asks about China. Management says it will recover, but that is a projection.
Original model output from the research archive. It may be verbose or include the model's working notes.
We need answer YES/NO. Need analyze transcript. Need determine if management points to specific identified sources of additional business already secured/in motion but contributed little or nothing to reported results, expected begin/ramp within roughly next year, so reported understate business already put in place. Need use only transcript. We need assess. Key candidates: defense bookings? RMD awards classified, SPY-6, RIS classified, etc. But those are backlog. Management says defense backlog strong. They discuss expected growth in defense spending but not yet orders? "we won't see any of that benefit this year" for replenishment. Need specific near-dated incremental contributors already secured/in motion. They mention "LTAMS unit delivered into U.S. Army test program" important milestone for franchise program. Not revenue yet? Program execution. But not necessarily contribution. They mention RTX Ventures launched, not revenue. They mention Russia sanctions reducing sales. No. They mention "first LTAMS unit" maybe future production. They mention "We have signed contract"? They mention "we recently delivered first LTAMS unit" - that is already delivered? Not future contribution. Need identify if management says "bookings" and "backlog" are already secured. In backlog, revenue recognized over time. Q1 backlog $154B. But does that understate? They point to specific new awards: RMD bookings $4.1B book-to-bill 1.18. RIS book-to-bill 0.8 but expect >1. These are already secured contracts, contributions will begin/ramp as programs execute. Management says defense backlog remained very strong at $62B exiting quarter. Does management indicate these will add meaningfully in coming quarters? They mention "we continue to expect RIS full-year book-to-bill to be greater than 1" and "RMD full-year book-to-bill at least 1.1". That's future bookings not secured yet. But current backlog is already in place. However if backlog is already reflected in reported results? Backlog not revenue; will contribute future. But not necessarily "little or nothing" to results just reported? Bookings in quarter are new, will contribute over years.
Original model output from the research archive. It may be verbose or include the model's working notes.
We need to determine if management conveys that the company is currently accumulating a queue of demand it cannot yet serve, and that management is spending energy managing that queue rather than finding demand. Key points from transcript: - Greg Hayes mentions supply chain constraints, but also mentions "we remain optimistic on the market recovery" and "air traffic is rebounding again". He talks about "resilient commercial air traffic, coupled with growing global defense budgets and our strong backlog". Backlog is reported but not necessarily a queue that is outrunning capacity. - Neil Mitchill discusses supply chain constraints, but also says "we continue to expect that the commercial aerospace recovery will remain resilient and will drive growth in commercial aftermarket and narrow-body OE deliveries this year." He mentions "we're closely monitoring China domestic and international traffic" and "our outlook assumes a significant improvement in wide-body traffic during the balance of the year." That is about expected demand, not current queue. - On defense, they talk about "strong backlog" and "book-to-bill" but that is routine reporting. They mention "we now expect RMD's full-year book-to-bill to be at least 1.1" which indicates orders coming in, but not necessarily that they can't serve them. - There is mention of supply chain constraints causing delays, but that is about inputs, not about customers waiting in line. - The only mention of a queue might be in the context of "we are on allocation for many of those components" (Greg Hayes) but that is about components, not customers. - No mention of customers waiting to be brought on, or a growing list of customers, or intake that cannot keep pace. The backlog is reported but not described as a problem of absorbing demand. - Management is focused on recovering from COVID, supply chain, and geopolitical issues, not on managing a queue of demand. Thus, the answer is NO.
Original model output from the research archive. It may be verbose or include the model's working notes.
我们根据提供的财报电话会议记录，判断管理层是否描述了当前增长由多个独立来源同时驱动，且这种广度是近期才出现的。记录中提到了商业航空售后市场、国防业务、国际国防支出等。管理层提到商业航空复苏、国防预算增加、国际盟友增加国防支出等。这些是多个独立来源。但需要确认是否“each described as already producing real current business”以及“breadth is itself recent or building”。管理层提到商业售后市场增长38%，国防业务有强劲订单，国际国防支出增加等。同时提到俄罗斯影响导致销售下调，但其他业务增长。管理层也提到“resilient commercial air traffic, coupled with growing global defense budgets and our strong backlog continue to support our long-term outlook”。这似乎表明多个引擎同时工作。但公司一直是多元化企业，是否“recently narrower”？管理层提到“two-year anniversary of our merger”，但并未明确说之前更窄。然而，管理层强调商业航空复苏和国防增长是当前驱动。可能符合。但需要谨慎：是否至少三个独立引擎？商业售后市场、国防（包括RMD和RIS）、国际国防支出？还有商业OE？但商业OE受供应链影响。管理层提到“commercial aftermarket remained strong”、“defense backlog very strong”、“international allies increasing defense spending”。这些是不同客户类型和地理。而且管理层说“we remain optimistic on the market recovery”等。但“breadth is itself recent or building”可能体现在商业航空复苏是近期，国防增长也是近期。但公司一直有国防和商业，所以可能不是“recently narrower”。然而，管理层提到“the impact of Omicron and increased geopolitical tensions”等，但并未说之前只有一两个引擎。我认为可能回答NO，因为公司一直是多元化，且管理层并未明确说之前更窄。但问题要求“management's own words convey this ONE phenomenon”。管理层说“Resilient commercial air traffic, coupled with growing global defense budgets and our strong backlog continue to support our long-term outlook”这确实表明多个引擎。但“recently narrower”没有明确。可能答案是否定的。再仔细看：管理层提到“commercial aftermarket remained strong in the quarter, growing 38%”和“defense backlog remained very strong at $62 billion”以及“international allies are also increasingly prioritizing defense spending”。这些是三个不同来源。但公司一直有这些业务，所以不是“recently narrower”。然而，管理层说“we remain optimistic on the market recovery”暗示之前受疫情影响，现在恢复，所以商业是恢复中的。国防是持续。但“breadth is itself recent or building”可能指商业恢复是新的，国防增长也是新的。但公司一直有国防。我认为可能不符合“recently narrower”的条件。因此回答NO。但为了准确，再读问题：“that the company was until recently narrower, with growth carried by fewer engines”管理层没有说之前更窄。所以NO。最终答案NO。
Original model output from the research archive. It may be verbose or include the model's working notes.
The question asks whether management describes the company as being at a point where the rate of accumulation has become the dominant fact, with both (1) management reaching for multiples when describing what's happening now, and (2) management treating the company as still small against what it's already doing. Let me examine the transcript for evidence of multiples framing. The key numbers mentioned: - Commercial aftermarket grew 38% in Q1 - Collins commercial aftermarket up 39%, with provisioning up 73%, parts and repair up 43% - Pratt commercial aftermarket up 37% - These are strong growth numbers but are they framed as multiples? The provisioning up 73% is a strong number but it's a single data point. The aftermarket growth of 38-39% is strong but not framed as "several times" or "doubled" in a way that suggests compounding off a small base. The company is a large, established aerospace and defense company with $67-68 billion in sales. It's not a young company scaling. The growth in aftermarket is recovery from pandemic lows, which is more of a recovery narrative than a compounding narrative. Management does not describe the company as "still small" relative to what it's doing. The company is huge. The aftermarket growth is recovery from COVID, not a new business scaling. The multiples cited (73% provisioning, 43% parts and repair) are strong growth rates but they're recovery from a depressed base, and management doesn't frame the company as small or early. The overall tone is a conventional results discussion with strong growth but not a compounding-off-a-small-base narrative. The answer is NO.
Original model output from the research archive. It may be verbose or include the model's working notes.
We need to determine if both conditions are met: (1) genuinely strong, concrete current business, and (2) thin or shallow professional audience around it. First, assess the business strength. The transcript shows strong Q1 2022 results: commercial aftermarket up 38%, sales up 4% organically, EPS up 28%, free cash flow better than expected. Defense backlog strong at $62 billion. Specific mentions: provisioning up 73%, parts and repair up 43%, etc. Management reports real operating strength with numbers. So condition (1) seems met. Second, assess the audience engagement. The call has many questioners: Myles Walton, Robert Spingarn, Peter Arment, Robert Stallard, Ron Epstein, Sheila Kahyaoglu, David Strauss, Kristine Liwag, Doug Harned, Noah Poponak, Mike Maugeri, Ken Herbert, Seth Seifman, Cai von Rumohr. That's 14 questioners. Each asks one question, but some have follow-ups? Actually, each questioner asks one question, but some are multi-part. The questions are substantive: supply chain, margins, Russia impact, China, etc. The analysts are probing and engaged. The call seems to have a full, active analyst audience. So condition (2) is not met. Thus answer NO.
Original model output from the research archive. It may be verbose or include the model's working notes.
我们根据提供的财报电话会议记录，判断管理层是否描述了公司现有业务中相当大的一部分仍以过去设定的价格、费率或条款进行，而这些条款现在明显低于当前市场条件下相同业务所能获得的价格，并且这些现有业务即将按照管理层已经可以看到的时间表进行重新定价、续约或重置，从而使公司的经济状况随着旧条款在未来几个季度的到期而改善，而无需赢得新客户或新需求。 在记录中，管理层讨论了通货膨胀、定价、供应链问题等。具体来说，Greg Hayes 在回答关于通货膨胀和定价的问题时提到，他们提高了售后市场的价格（目录价格），大约上涨了6%，以抵消通货膨胀。他还提到，他们通常在10月份调整价格，并在1月份生效。他提到，如果通货膨胀持续，他们将在10月份再次调整目录价格。这似乎是对现有业务（售后市场）的定价调整，但这是否是“过去设定的条款现在低于市场条件”的情况？管理层没有明确说现有合同或协议是以低于当前市场的价格签订的，而是说他们提高了价格以应对通货膨胀。他们也没有描述一个明确的“重置时间表”，而是说他们每年调整一次价格。此外，他们提到长期协议（LTA）占商业航空航天供应的80%，这些协议有固定的价格调整机制，但管理层没有说这些LTA的条款低于当前市场，而是说他们通过谈判和成本削减来应对通货膨胀。 在回答关于俄罗斯制裁的影响时，他们提到失去了俄罗斯的销售，但这是关于失去业务，而不是重新定价。 在回答关于RMD利润率的问题时，他们提到由于供应链问题，EAC（估计完工成本）较低，但这是关于成本，而不是定价。 在回答关于商业售后市场的问题时，他们提到售后市场增长强劲，但这是关于数量，而不是价格。 在回答关于通货膨胀的问题时，Greg Hayes 说：“我们提高了价格，目录价格大约上涨了6%。” 这似乎是对现有业务的定价调整，但这是否是“过去设定的条款现在低于市场条件”？他们提高价格是因为通货膨胀，而不是因为过去的价格低于市场。他们也没有描述一个“重置时间表”，而是说他们每年调整一次。 此外，在回答关于RMD的问题时，他们提到“合同组合”导致利润率下降，但这是关于不同合同的组合，而不是关于现有合同重新定价。 在回答关于商业OE的问题时，他们提到产量增加，但这是关于数量，而不是价格。 因此，管理层没有描述一个明确的“现有业务以低于市场的价格签订，并将在未来重置”的情况。他们提到的是应对通货膨胀的定价调整，以及由于供应链问题导致的成本增加，但这不是关于现有合同重新定价到更高水平。他们也没有描述一个“时间表”来重置这些条款。 因此，答案应为NO。
Original model output from the research archive. It may be verbose or include the model's working notes.
We need to determine if management describes future business that counterparties have already committed to, large relative to current business, with conversion within a year, and that management presents as such. Scanning the transcript: There are mentions of backlog ($154 billion total, $62 billion defense), bookings, and awards. But are these described as large relative to current business and with near-term conversion? The defense backlog is routine for this company. The commercial aftermarket recovery is demand-driven, not committed. The Russia impact is a reduction. No mention of a specific large committed contract or expansion that is disproportionate. The talk about defense spending increases is future opportunity, not committed. The GTF production rates are based on customer schedules but not described as a large committed expansion relative to current. The answer is NO. Thus, answer NO.
Original model output from the research archive. It may be verbose or include the model's working notes.
We need to determine if the transcript describes a situation where the company is currently in the middle of copying a part of its own business that is already working into additional places where the same demand is already visible. The key is: a proven instance, copies being stood up now, and demand already identified at the destination. Let's scan the transcript for any such description. The call covers results, supply chain issues, Russia impact, defense budgets, etc. There is no mention of replicating a proven business unit, opening new facilities, expanding into new markets with existing proven models, etc. The discussion is about existing operations, supply chain constraints, and outlook. No mention of standing up copies of anything. So answer NO.
Original model output from the research archive. It may be verbose or include the model's working notes.
The question is: Does management describe that the company's OWN COMMERCIAL EFFORT HAS SHIFTED FROM PERSUADING BUYERS TO PROVISIONING THEM? That is, the work is figuring out how to GET MORE OF ITS OFF...YES The transcript shows clear evidence of committed buying: commercial aftermarket sales grew 38% YoY and 5% sequentially, driven by provisioning (73% increase), parts/repair (43%), and modifications (11%); commercial OE sales rose 12% at Collins and 12% at Pratt with narrow-body 787 deliveries and GTF production; defense backlog stood at $62 billion with $4.1 billion RMD bookings (book-to-bill 1.18) and $2.6 billion RIS bookings (book-to-bill 0.
Original model output from the research archive. It may be verbose or include the model's working notes.
The user asks if the company is talking about demand being "off the charts" and if the numbers support that claim. I need to check the transcript for any mention of "off the charts" or similar language about demand. The transcript discusses commercial aerospace recovery, defense budgets, and supply chain constraints, but there is no explicit statement about demand being "off the charts." The numbers show growth in aftermarket and OE, but the company also notes challenges like supply chain issues and reduced sales outlook due to Russia. Therefore, the claim is not supported by the data provided.
Original model output from the research archive. It may be verbose or include the model's working notes.
We need answer only yes/no. Need evaluate gates. Gate1: is company talking about demand off the charts? No, they talk recovery, strong aftermarket, but not off the charts. So NO. Final NO.
Original model output from the research archive. It may be verbose or include the model's working notes.
我们根据提供的电话会议记录，判断公司领导层是否相对较新，并且是否已经做出了具体的改变并取得了早期成果。记录中，Gregory Hayes 是董事长兼首席执行官，Neil Mitchill 是首席财务官。没有提到领导层最近更换。Gregory Hayes 在电话会议中谈论了公司的战略和运营，但没有提到他自己是新任领导。因此，答案应为 NO。
These archived questions are separate from the newer 500-check scan above.
Open a quarter only when you want its detailed evidence.
| Call | Date | Grade | Guidance | Demand | Margins | Candor | Evasion |
|---|---|---|---|---|---|---|---|
| Q4 2024 | 2025-01-28 | C+ | maintained | accelerating | expanding | 7/9 | 3/9 |
| Q3 2024 | 2024-10-22 | B+ | raised | accelerating | expanding | 7/9 | 3/9 |
| Q2 2024 | 2024-07-25 | B | raised | accelerating | expanding | 7/9 | 3/9 |
| Q1 2024 | 2024-04-23 | C | maintained | accelerating | expanding | 7/9 | 4/9 |
| Q4 2023 | 2024-01-23 | D | lowered | accelerating | contracting | 7/9 | 3/9 |
| Q3 2023 | 2023-10-24 | B | raised | stable | contracting | 7/9 | 2/9 |
| Q2 2023 | 2023-07-25 | D | mixed | accelerating | expanding | 8/9 | 4/9 |
| Q1 2023 | 2023-04-25 | C+ | maintained | accelerating | expanding | 7/9 | 3/9 |
| Q4 2022 | 2023-01-24 | B+ | maintained | accelerating | expanding | 8/9 | 2/9 |
| Q3 2022 | 2022-10-25 | C | lowered | accelerating | contracting | 8/9 | 3/9 |
| Q2 2022 | 2022-07-26 | D | maintained | mixed | contracting | 8/9 | 3/9 |
| Q1 2022 | 2022-04-26 | C+ | lowered | mixed | expanding | 8/9 | 2/9 |
| Q4 2021 | 2022-01-25 | B | maintained | accelerating | expanding | 7/9 | 2/9 |
| Q3 2021 | 2021-10-26 | A | raised | accelerating | stable | 8/9 | 2/9 |
| Q2 2021 | 2021-07-27 | A | raised | accelerating | expanding | 7/9 | 2/9 |
| Q1 2021 | 2021-04-27 | B+ | raised | accelerating | contracting | 7/9 | 3/9 |
| Q4 2020 | 2021-01-26 | C+ | maintained | mixed | contracting | 7/9 | 2/9 |
| Q3 2020 | 2020-10-27 | B | maintained | mixed | contracting | 8/9 | 2/9 |
| Q2 2020 | 2020-07-28 | C | withdrawn | slowing | contracting | 8/9 | 2/9 |
| Q1 2020 | 2020-05-07 | C | withdrawn | mixed | contracting | 8/9 | 2/9 |