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+# Transcript Forensics · product overview
+
+## Pitch
+
+Audio counterpart to Blocao video forensics. Recordings of meetings, phone calls, depositions are processed on-site by Whisper + pyannote. The console offers natural-language query ("what was said about contract Acme and who was most insistent") with speaker identification, topic detection, and waveform-anchored playback.
+
+Built on the **same platform** as Blocao: same router, same Cell hardware (with a microphone or audio ingest interface), same MQTT bus, same GitOps, same hub.
+
+## What you get
+
+- **Transcript console** with chat-style query, waveform with speaker bands, transcript navigable by speaker, audio player with live caption.
+- **Speaker library**: voiceprints stored per-customer, automatically matched against new sessions.
+- **Topic detection**: NER + clustering surfaces what was discussed; per-speaker breakdown.
+- **Insistence ranking**: identifies who pushed hardest on each topic (intervention rate, repetition).
+- **Pin to case**: same case management as Blocao video.
+- **Cross-product correlation** (Year 2): correlate audio transcripts with video events at a site.
+
+## Use cases
+
+1. **Meeting forensics for legal/compliance**: review of depositions, board meetings, contract negotiations.
+2. **Customer service quality**: search support call recordings for problematic patterns.
+3. **Mediation and arbitration**: who said what, when, with audio proof.
+4. **Insurance claim review**: verify what was promised in pre-purchase calls.
+5. **Internal investigations**: HR or compliance review of recorded conversations.
+
+## Why same platform as Blocao
+
+The transcriptor and Blocao share enough infrastructure that running them on separate stacks would be wasteful:
+
+- Same router, same Cell, same MQTT, same GitOps, same hub.
+- Same console patterns (chat, waveform/timeline, player, case management).
+- Same sovereignty model.
+- Same evidence chain (post-MVP).
+
+The only product-specific pieces are:
+
+- The Whisper + pyannote container in the Cell stack.
+- The speaker library schema in Qdrant.
+- The transcript-specific console panel.
+
+See [`shared-stack.md`](shared-stack.md) for the breakdown of shared vs distinct.
+
+## Differentiators
+
+| Vs | Transcript Forensics differentiator |
+|---|---|
+| **Otter.ai / Sonix / Fireflies** | On-site processing + sovereignty + speaker library across sessions |
+| **Microsoft Azure Speech / AWS Transcribe** | No vendor lock-in + open-source models + same UX as Blocao |
+| **Open-source Whisper alone** | Fleet-managed + speaker identification + topic detection + UX |
+
+## Status
+
+Mockup complete (`mockups/transcript-forensics.html`). Backlog at `backlog/transcript-sprint-backlog.md`. Many stories shared with Blocao backlog (see `backlog/shared-stories.md`).
+
+The transcript product launches **after** Blocao MVP is shipping (target: Year 2). Until then, design lives in this repo to ensure the platform is built with audio in mind from day one.