#1 Product of the day

#1 DevTool of the week

"without disrupting the day-to-day work of your engineers"

Know what's changing, what needs attention, and why delivery slows using real-time signals and on-demand or scheduled reports prompted in human language.

Ask anything about your delivery pipeline

Which PRs are exceeding the expected review cycles this week?

No custom setup

No YAML. No workflows to redesign

Minutes to first value

Schedule a walkthrough See example questions →

GitHub

Slack

Linear

Jira

Warestack works with over 20 companies through integrations, usage, and partnership programs.

Everything you need to manage your engineering teams

From natural language queries to raw SQL access, Warestack gives you full control over your development data.

Generate Report with AI

Describe what you want to analyze and we'll generate a custom report

e.g., List all PRs merged without review in the last 30 days

Self-merged PRsFailed deploymentsSlow reviewsCritical violations

Weekly PR Analysis

Weekly

Summary of pull request activity and review times

Last run 2 hours agoNext Dec 18

Deployment Metrics

Daily

Track deployment frequency and success rates

Last run 6 hours agoNext Tomorrow

Team Performance

Monthly

Individual and team contribution analytics

Last run 3 days agoNext Jan 1

Vibe coding is unpredictable. Code reviews are for humans!

PROBLEM

Reviews are inconsistent

Pull request signals are scattered

Your codebase lives in GitHub, discussions in Slack, and issues in Linear.

SOLUTION

The Engineering Data Layer

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.

250K+PRs & Reviews

170K+Issues & Tickets

350K+Slack Threads

90K+Deploy Events

DORA Metrics

SOC 2 Audit Trails

Cycle Time Analysis

Team Performance

Behavioral Metadata

Enrich with behavioral pattern metadata

Warestack doesn't just store events—it enriches them with behavioral patterns, associations, and computed metrics like cycle time, review velocity, and deployment frequency.

PR → Issue → Deploy lineage tracking

Auto-computed DORA metrics per team

Show cycle time for PRs merged this week

Warestack Enriched Schema+ metadata

{
  "pull_request": {
    "id": "PR-4832",
    "title": "feat: add auth middleware",
    "author": "mary@acme.com",
    "status": "merged",
    "created_at": "2025-10-12T09:14:00Z",
    "merged_at": "2025-10-14T11:30:00Z"
  },
  "warestack_metadata": {
    "cycle_time_hours": 50.3,
    "review_rounds": 2,
    "linked_issue": "LIN-294",
    "deployment_id": "deploy-9821",
    "first_review_at": "2025-10-12T14:22:00Z",
    "team": "backend",
    "patterns": ["security-change", "large-diff"]
  }
}

FEATURE

Agentic Checks

Checks & agreements that guide your team

Before

Rules scattered in YAML files, sticky notes, and README docs.

Now

Warestack automatically suggests checks that explain risk in plain English. Activate what makes sense for your team.

Rules in READMEs

- drive-by approvals
- PR diff = Linear scope

warestack

Approved without actually reviewing the code.

CRITICAL

Warestack found a signal →review.file_views == 0.

Enable Warestack blocks

A dependency file has been modified.

Warning

A change detected that doesn't match issue scope.

Enable Warestack flags

4,703 repos in sync

We process thousands of GitHub operational events for our customers, capturing hundreds of contextual signals for every PR.

Why Warestack?

Monitor DevOps processes, not just errors—with context-aware governance.

Feature	Warestack	AI code review tools	GitHub (native features)
Process monitoring (How, Why, When)	Tracks DevOps processes—how changes were made, why decisions were made, and under what conditions. Links PR → review → deployment → incident chains.	Focus on individual PRs; no cross-event correlation or workflow context.	Tracks what changed (code, metadata); no process context or post-merge chain linking.
Explainability	Deterministic checks + evidence recorded on PRs/issues.	Opaque AI responses; difficult to trace why suggestions were made.	No built-in explainable policy engine.
Human-language queries	Search and ask in plain English across PR history.	Limited query capabilities; typically focused on current PR only.	No query layer.
Context-aware rule enforcement	Adaptive rules that consider developer role, timing, and team context. Enforce based on real-world conditions, not just binary checks.	Provide suggestions and comments; cannot enforce or block merges.	Binary branch protection (yes/no); cannot adapt to context like developer seniority or deployment timing. Protection rules end at merge button.
Operational agents (Slack/Linear)	Post actionable comments into Linear and Slack with context.	Basic notifications; limited integration with workflow tools.	Notification-only.
Centralized audit trail	Centralized evidence trails with full context—who violated what rule, when, why, and what it impacted. SOC-2 ready workflows.	Limited audit trails; difficult to prove why decisions were made.	No centralized audit bundle across tools; violations scattered across repos.