Documentation
Intelligence & coaching
Rules engine, optional LLM thread analysis, recommendations, scorecards.
Layers
| Layer | Package / route | Requires model keys? |
|---|---|---|
| Deterministic alerts | @chatalyze/metrics alerts | No |
| Revenue recommendations | recommendActions | No |
| Thread analysis (Rules provider) | RulesModelProvider | No |
| Thread analysis (HTTP LLM) | HttpChatModelProvider | Yes — all of MODEL_API_BASE,MODEL_API_KEY, MODEL_NAME |
| Batch run | POST /api/admin/intelligence/run | Optional |
Structured thread analysis
Model (or rules) output shape:
{
"stage": "rapport|qualify|offer|objection|close|aftercare|unknown",
"fan_intent": "string",
"chatter_quality": 1-5,
"issues": ["…"],
"next_best_action": "…",
"risk": ["…"],
"revenue_opportunity": "high|med|low",
"summary": "…"
}Run intelligence
curl -X POST https://api.chatalyze.co/api/admin/intelligence/run \
-H "Authorization: Bearer $ADMIN_TOKEN" \
-H "content-type: application/json" \
-d '{"limit":10}'
# or single session
-d '{"sessionId":"sess_…"}'- Without
sessionId, selects up tomin(limit ?? 10, 20)open sessions - Persists labels to
rm_model_labelsand raises durable alerts - Never called from the webhook path
Recommendations
Kinds: fan · chatter · shift ·playbook. Examples: prioritize whale awaiting reply; soft-close when warm with no CTA; escalate off-platform risk.
Scorecard
GET /api/chatters/scorecardPer chatter: sessions, revenue, conversion rate, SLA breach rate, alert counts, coachPriority =crit×3 + warn×2 + info + slaBreachRate×10.
Session analysis
GET /api/sessions/:id/analysis
# metrics + alerts + labels + recommendations