The bugs that wreck a week are rarely the ones that throw. Exceptions get a stack trace, a Sentry event, and a clear line number. The expensive ones arrive as a sentence from a customer: the invoice total is wrong, the dashboard shows last month's numbers, a record that should exist is gone. Nothing crashed. There is nothing to grep for.
Why these bugs are so slow to fix
A no-error bug has no anchor. With a stack trace you start at the failing line and walk backwards. With a wrong number you have to first prove what 'correct' even looks like, then reconstruct how the data got into its current state. That means jumping across the database, recent deploys, GitHub history, and logs, holding the whole timeline in your head. It is the most senior engineer on the team doing manual archaeology, and it routinely costs one to three hours per incident.
A repeatable way to investigate
Start from the artifact, not the code. Pull the exact wrong record and write down precisely which field is wrong and by how much. A $40 discrepancy that is always a round fraction of the line items points at rounding, not data loss.
Bracket the time window. Find the first record that shows the wrong behavior and the last one that does not. That boundary almost always lines up with a deploy. Now you have suspects: the PRs that shipped in that window.
Diff the suspects against the symptom. You are not reading every line. You are looking for the one change that could produce this specific distortion: an order-of-operations flip, a rounding step that moved, a filter that changed scope. Confirm it against more affected records before you believe it.
Why tooling has not solved this
Error-triage tools need an event to start from, and there is no event. Enterprise AI SRE platforms start from an alert or a telemetry anomaly, and there is no alert either. Static code-search tools answer questions about the code but never touch the live state where the bug actually lives. The no-error escalation falls in the gap between all of them.
That gap is exactly what Treap works on: a read-only investigator that starts from a human message in Slack, correlates across your code, logs, and database, and replies with a suspected root cause, the evidence it checked, and a confidence score. If this is the class of bug that eats your week, we would like you on the early-access list.