# The Recorded Call: One Input, Dozens of Outputs

## The Missing Layer

We have async communication covered — tasks, comments, notes, messages. We have file storage, AI transcription on the roadmap, search across everything. But there's a gap: real-time voice and video calls that become part of the system instead of disappearing the moment someone hangs up.

Every team has calls. Most of those calls vanish. Someone might take notes, maybe share them, probably forget half of what was said. The decisions made on Tuesday's call are already lost by Thursday. The question someone asked gets asked again next week because nobody recorded the answer.

What if the call itself became data?

## What Happens When You Record a Call

A single 30-minute team call, run through the right processing pipeline, could produce:

| Output | How | Where It Lands |
|--------|-----|-----------------|
| Full transcript | Speech-to-text (Whisper) | File attached to project |
| Summary | AI summarization | Note on the project |
| Action items | AI extraction | Tasks created automatically |
| Questions asked | AI detection | Searched against existing knowledge base |
| Unanswered questions | No match found | Follow-up tasks created |
| Decisions made | AI extraction | Comments on relevant existing tasks |
| Names mentioned | Entity recognition | Linked to contacts |
| Dates/deadlines mentioned | Temporal extraction | Events created in calendar |
| Key moments | Timestamp marking | Bookmarks within the recording |
| Video thumbnails | Frame extraction | Filmstrip for visual browsing |

One input. Potentially dozens of useful outputs. Every single one landing in a system that already exists and is already searchable.

## The Cascade Effect

This isn't one agentic loop — it's a cascade. Each output feeds into other processes:

**Transcript** → gets searched next time someone asks "what did we decide about X?"

**Tasks extracted** → assigned to team members → tracked to completion → referenced in next call's context

**Questions surfaced** → matched against existing articles and notes → answers presented automatically → gaps identified for documentation

**Contacts linked** → next time you look at a contact, you see every call they were on and what was discussed

**Summary notes** → feed into weekly/monthly reports → inform project status → available to AI agents for context

The system gets smarter about your projects over time because it was listening. Every call adds to the collective knowledge base. The fifth call about a project has the context of the first four.

## Why This Matters Now

Six months ago, this would have been a feature spec on a whiteboard. Today, the receiving infrastructure already exists:

- **File storage**: S3 pipeline handles video/audio files
- **Transcription**: Whisper API integration is planned, the task template pattern is built
- **Task creation**: API and templates can create tasks from any trigger
- **Notes**: Created programmatically, attached to projects
- **Contacts**: Linked and searchable
- **Events**: Calendar system is live
- **Search**: Global search across all content types
- **Video processing**: Filmstrip/thumbnail generation is next up
- **AI agents**: Can process, summarize, extract, and route information

The hard part isn't building any one of those processing steps. The hard part was building a system where they all have somewhere to land. That's done.

## The Real-Time Piece

The call itself needs:

1. **WebRTC or similar** for browser-based video/voice (no app install)
2. **Recording** that streams to storage in real-time
3. **Live transcription** running alongside the call (optional, but powerful)
4. **Screen sharing** for collaborative work
5. **Chat sidebar** that becomes part of the record

Django Channels with WebSocket support is already running. The real-time infrastructure exists. The recording is just a media stream being piped to S3 while the call is active.

## What Changes for Teams

**Before**: Call happens → someone maybe takes notes → notes maybe get shared → action items maybe get tracked → context is maybe remembered next time.

**After**: Call happens → everything is automatically captured, processed, and distributed to the right places in the system. Nothing is lost. Nothing requires someone to remember to write it down.

The meeting becomes a first-class data source, not a black hole that absorbs an hour of everyone's time and produces nothing searchable.

## The Bigger Picture

This is the same pattern we keep seeing: raw input goes into the system, gets processed through multiple AI-powered steps, and produces structured, searchable, actionable outputs. Images, videos, documents, and now conversations — they're all just inputs to the same machine.

The platform doesn't care if the data came from a file upload, an API call, an AI generation, or a live conversation. It all goes through the same pipeline: store it, process it, connect it to projects and tasks, make it findable, make it useful.

A recorded call isn't a feature. It's just another input type for a system that already knows what to do with information.

---

*Built on [AskRobots](https://askrobots.com) — where every input becomes a searchable, actionable asset.*