Platform
Observe
See how your AI handles emotion in production. Observe provides visibility into emotional behavior across your interactions — drift detection, escalation coverage, and intensity patterns that traditional monitoring misses.
The Visibility Problem
AI systems handle emotionally consequential interactions every day. But traditional monitoring tools track errors, latency, and token counts — not whether emotional handling changed after a model upgrade, or whether escalation rules are firing correctly.
The question Observe answers: Did emotional handling drift after your last deployment? Are guardrails overfiring or underfiring? Which high-intensity moments went unhandled?
Input Modes
DeepaData accepts content in different structures depending on your integration pattern. The input mode determines what gets captured and how observation triggers fire.
Topic-shift triggered
The /v1/observe endpoint captures salience at topic-shift boundaries. Lightweight "what changed + why it matters" records without full artifact extraction.
Best for: High-volume chat, continuous monitoring, escalation detection
Passage/session-based
The /v1/extract endpoint processes complete passages or session transcripts into full 96-field EDM artifacts.
Best for: Therapy sessions, long-form journaling, retrospective analysis
Batch processing
The /v1/batch/upload endpoint processes historical content retroactively. Upload JSON or CSV with up to 1,000 records.
Best for: Historical data governance, migration, compliance backfill
Observation Triggers
The Observe endpoint detects 7 trigger types that indicate when emotional context has shifted or requires attention.
| Trigger | Type | Description |
|---|---|---|
| topic_shift | content | Conversation topic changed meaningfully |
| affect_shift | content | Emotional intensity or valence changed significantly |
| risk_signal | content | Content indicates potential harm or distress |
| recurrence | content | Theme or pattern has appeared before |
| user_mark | caller | Caller explicitly requested capture (force: true) |
| session_close | caller | Session ending, capture final state |
| time_gap | caller | Significant time elapsed since last observation |
Content-detected triggers are identified by the LLM. Caller-provided triggers are passed via request parameters (is_session_close, time_since_last_ms, etc.).
Using the Observe API
curl -X POST https://www.deepadata.com/api/v1/observe \
-H "Authorization: Bearer dda_live_YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{
"content": "I have been feeling much better about work lately...",
"subject_id": "user-123",
"session_id": "session-456",
"is_session_close": false
}'Dashboard Metrics
The Observe dashboard in the platform console surfaces five key metrics from your extraction and observation data.
Emotion Distribution
Primary emotions across extractions
Trigger Distribution
What triggers salience capture
Escalation Coverage
% flagged for escalation
Intensity Histogram
Emotional weight distribution
Observe Products
Beyond the overview dashboard, Observe includes specialized analysis tools.
Drift Detection
Compare emotion intensity distributions across time windows. Detect silent drift after model upgrades or configuration changes.
Escalation Matrix
Correlation view of intensity score vs escalation action. Identify gaps (high emotion, no response) and noise (low emotion, over-response).
Coverage Report
Guardrail coverage analysis. % of high-intensity passages that triggered escalation vs false positives.
Emotional Exposure
How many emotionally consequential interactions this period, by domain and jurisdiction.