Built for engineering leadership
The unified schema enables metrics and reports that were previously impossible without expensive data engineering projects.
DORA Metrics
Calculate deployment frequency, lead time for changes, change failure rate, and mean time to recovery—automatically from your Git and CI data.
"What was our lead time for changes last quarter?"Compliance Audit Trails
Generate SOC 2 and HIPAA audit reports showing who changed what, when, with full approval chains. Zero manual documentation.
"Show all production changes requiring security review"Cycle Time Analysis
Track ticket-to-deploy lead time, identify bottlenecks in code review, and measure cross-team handoff delays.
"Which team has the longest review-to-merge time?"Executive Reports
Generate weekly engineering reports for leadership with delivery velocity, risk indicators, and team performance trends.
"Send me a weekly summary of blocked PRs"Stop context-switching between GitHub, Jira, Linear, and Slack. Warestack builds a unified lineage for every PR with metadata, associations, and behavioral patterns—queryable with SQL or natural language.
Normalized Schema
All events are transformed into Warestack's canonical schema—a structured model that maps PullRequest, Review, Commit, Issue, WorkflowRun into SQL tables with deterministic joins.
Raw and Unified Data
Continuously captures GitHub PR metadata, CI/CD logs, Slack discussions, and project management updates. Access data as raw event streams (JSON/Webhooks) or explore in Warestack's unified dashboard.
ML-Enriched Metadata
Every data object is enriched with computed fields like review_latency, loc_changed, is_afterhours, and risk_score. ML models detect behavioral patterns and anomalies.
Cross-System Correlation
Correlates signals across systems—linking code changes to communication, deployments, checks, and incidents. Trace relationships between PRs,failed_deploy events, and rushed_merge patterns.