High-Performance Teams

Monitor cognitive load in high-stakes environments and provide clear next steps.

Journey (engineering / high-performance flow)

json
Weekly check-ins (retro notes, standup summaries, PR comments)
    → Burnout scoring (exhaustion, detachment, efficacy)
    → Blocker extraction (cognitive load, tool churn, on-call fatigue)
    → Learning pathways (AI upskilling, architecture refreshers, role rotations)
    → Lead brief (trend, blockers, suggested moves)
    → Follow-up pulse (3–5 sentences, bi-weekly)

Challenges we watch

  • Cognitive overload: rapid tool/language shifts, multi-track projects.
  • On-call fatigue: pager load, sleep debt, incident aftercare gaps.
  • Detachment: “shipping but not learning” tone, cynicism about roadmap.
  • Growth stalls: unclear progression, low confidence on new AI/infra.

Interventions we return

  • Workload shaping: rotate out of hot on-call, reduce parallel projects.
  • Upskilling tracks: short AI/infra pathways mapped to current stack.
  • Pairing moves: senior + mid pairing on complex refactors; doc sprints.
  • Recovery: decompressed sprints after high-incident periods.

Inputs that work best

  • 40–120 words from retro notes, PR self-reviews, post-incident reflections.
  • Avoid sensitive identifiers unless contractually cleared; use anonymous IDs.

Example request

json
curl -X POST https://api.stageonego.com/v1/detect \
  -H "Authorization: Bearer $STAGEONEGO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Juggling a migration and two AI prototypes. Pager was noisy all week."
  }'

Example response (trimmed)

json
{
  "data": {
    "burnout_intensity": {
      "score": 0.48,
      "risk_level": "yellow",
      "trend": "rising"
    },
    "symptoms": ["emotional_exhaustion", "cognitive_friction"],
    "incoming_blockers": ["context_switching", "on_call_fatigue", "tool_churn"]
  },
  "meta": {
    "request_id": "req_eng_123",
    "timestamp": "2024-01-15T10:35:00Z"
  }
}

Safeguards

  • Decision support only; keep managers and HR in the loop.
  • No raw-text retention by default; enable storage explicitly if required.
  • Respect local escalation paths for severe distress signals.

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