> ## Documentation Index
> Fetch the complete documentation index at: https://docs.kaipassport.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Data architecture

> How Kai processes, aggregates, and transports affective signals

Kai Affective Intelligence is designed so raw content never persists. Signals are extracted in memory, aggregated at the cohort level, and delivered via REST API.

## Signal source

Customer content streams — Slack messages, WordPress comments, survey responses, support tickets. Content is sent to Kai via `POST /ingest`. Raw text is processed in memory and never written to any database.

## Kai Brain

Kai's emotional intelligence framework classifies each item: mood, psychological state cluster, topic, and distress score. The same framework that powers Kai's AI companion, applied to external content without requiring user history.

## Pseudonymous uids

Model B customers assign a random uid per user before sending content. Kai tracks signals per uid over time to detect trends and escalation. Kai never holds re-identification keys — the customer does.

## Aggregation

Supabase Edge Functions aggregate per-uid signals into cohort-level JSON on a nightly schedule and on-demand. Enforces anonymisation floor before writing to cache.

## Transport

REST API with JSON payloads. Webhook support for real-time escalation alerts via HTTPS POST to customer's nominated endpoint.

## Architecture overview

```
Content streams → POST /ingest → Kai Brain (in-memory) → Aggregation → Cache → GET /pulse / GET /alerts
                                      ↓
                              Webhook alerts (HTTPS POST)
```

<CardGroup cols={2}>
  <Card title="Ingestion" icon="upload" href="/api-reference/ingest-batch">
    Send content batches or real-time streams for analysis.
  </Card>

  <Card title="Output" icon="chart-line" href="/api-reference/pulse">
    Retrieve aggregated mood, state, topic, and action signals.
  </Card>
</CardGroup>