Pagerly

Pagerly integrates directly with Slack and Microsoft Teams chat platforms to automatically surface relevant incident context, logs, metrics, and runbook information within the chat interface where oncall engineers are already working. It uses chat platform APIs and webhooks to listen for incident triggers and inject contextual information without requiring context-switching to external tools.

Pagerly analyzes incident patterns, historical resolutions, and current system state to generate contextual prompts and debugging suggestions directly to the oncall engineer. It uses machine learning or rule-based pattern matching on incident history and system topology to recommend next debugging steps, relevant team members, or previous solutions without explicit user request.

Pagerly connects to multiple monitoring, logging, and observability platforms (Datadog, New Relic, Prometheus, CloudWatch, Splunk, etc.) and normalizes their disparate data formats into a unified schema for presentation in chat. It handles authentication, API polling, data transformation, and caching to present consistent incident context regardless of underlying tool fragmentation.

Pagerly maintains an indexed repository of runbooks, playbooks, and documentation and uses incident metadata (service name, error type, severity) to automatically retrieve and surface the most relevant runbook in chat. It uses semantic or keyword-based matching to connect current incidents to historical solutions and operational procedures without requiring manual search.

Pagerly integrates with oncall scheduling systems (PagerDuty, Opsgenie, Grafana OnCall) or maintains its own rotation schedule to track who is currently on-call and automatically route incidents to the right person. It supports escalation policies, team hierarchies, and skill-based routing to ensure incidents reach the appropriate responder without manual assignment.

Pagerly maintains a structured incident timeline within Slack/Teams threads, capturing all actions, decisions, and communications related to an incident in a single consolidated view. It automatically logs state changes, integrations with external systems, and team communications to create an audit trail and post-incident review record without requiring manual documentation.

Pagerly analyzes incoming alerts from multiple sources and applies deduplication logic to suppress duplicate or related alerts that would otherwise flood the oncall engineer with redundant notifications. It uses alert fingerprinting, correlation rules, and configurable thresholds to group related alerts and surface only the most critical or unique incidents.

Pagerly analyzes incident characteristics (affected services, error rates, customer impact, system load) to automatically assess or suggest incident severity and priority levels. It uses configurable rules, historical impact data, or ML-based models to classify incidents and route them appropriately without relying on manual severity assignment.