App Store reviews, NPS verbatims, Zendesk tickets, interview notes, community mentions — five inputs, five biases, five cadences. Treat them equal and the loudest channel wins. The fix is a normalization and weighting layer that produces one weekly brief.
Twenty-six retros a year, three platforms, zero memory. The same friction points keep resurfacing because nobody re-reads 26 documents. An agent that normalizes, clusters, and ranks the patterns turns retro output into a longitudinal record the team can act on.
Every signal that would have caught the bad hire was already in your stack — sitting in scorecards nobody opened, Slack threads buried under 200 others, comp data in a different tool. The synthesizer compresses it into one structured recommendation before the offer goes out.