through-line

Consequential conversations go wrong quietly. On an order call, a buyer says Friday is a hard deadline at minute two. Twenty minutes later, asked about Monday delivery, they answer "Yeah... I guess." Nobody lied. The call ends, and operations ships against terms nobody actually agreed to: disputes, rework, chargebacks. Transcription tools answer "what was said?" Throughline answers "what changed?" It keeps a temporal memory of the claims, commitments and constraints a conversation establishes, and flags a later turn that conflicts with them while the terms are still negotiable. Others check speech against a rulebook; Throughline checks a conversation against itself. The engineering claim: the LLM proposes, a deterministic engine decides. An extraction model on AssemblyAI's LLM Gateway turns utterances into proposed events, but only the evidence gate may raise an alert, and only when it resolves to two real, distinct, chronologically ordered utterances in the transcript. No citable evidence, no alert. If diarization later reassigns a speaker, the gate re-runs and withdraws any alert its evidence no longer supports. We publish the measurements, including the failure. Over three live extraction runs, commitment drift fired 3/3, a clean control loaded with near-misses stayed silent 3/3, and contradiction fired 0/3. A weak proposer yields missed alerts, never false ones. AssemblyAI streaming with diarization was verified against the live API at 7/7 speaker attribution. The demo replays three scripted deal calls through the real engine and evidence gate, showing the agent's memory as it builds and every alert with its two cited quotes. The engine is a pure reducer with 59 tests and no network dependency, and the repo carries 28 decision records, reversals included.