Define Support Tiers
Tier 0: automated detection and self-healing (retries, circuit breakers). Tier 1: runbooks handled by on-call ops. Tier 2: pod-level escalation for data or AI-specific issues. Everyone knows their role before an incident happens.
Publish SLOs for each surface: API latency, ingestion throughput, copilot response time, retrieval accuracy. Instrument them in Application Insights and pipe alerts into PagerDuty or Opsgenie.
- Store runbooks and KRs in the same knowledge base as FAQ so copilots can guide ops.
- Rotate on-call among pod members so knowledge spreads.
- Hold monthly post-incident reviews to update runbooks and backlog.
Monitor What Matters
Dashboards track ingestion success rate, vector DB health, model latency, integration queue depth, and user-collected feedback. When a metric deviates, alerts tag the right owner immediately.
Collect qualitative feedback via in-app thumbs up/down, Slack bots, and quarterly stakeholder interviews. Feed this into a support backlog prioritized by impact.
- Tag incidents by root cause (integration, data, model, UI) to spot patterns.
- Automate a weekly health report summarizing uptime, incidents, and feedback.
- Schedule retraining or prompt tuning based on drift indicators.
Evolve With The Business
When new products launch or acquisitions happen, run impact workshops to map changes into the copilot and data agenda. Update ingestion seeds, access controls, and dashboard slices accordingly.
Maintain a quarterly innovation sprint where the pod experiments with new system integrations or AI capabilities using live feedback from support logs.
- Keep a public change log so users know what improved each month.
- Align support metrics with business metrics (renewal rate, CSAT) so investments stay funded.
- Use copilots internally to triage tickets and recommend next best actions for support teams.