The engineering data layer

Ingest events from GitHub, Linear, and Slack into a single queryable schema — calculate DORA metrics, trace ticket-to-deploy lead times, and generate reports from one source of truth.

DORA Metrics

"What was our lead time for changes last quarter?"

Compliance Audit Trails

"Show all production changes requiring security review"

Cycle Time Analysis

"Which team has the longest review-to-merge time?"

Executive Reports

"Send me a weekly summary of blocked PRs"

What this enables

Cross-tool event correlation

Link a Slack decision to the PR that implemented it, back to the Linear ticket that specified it. One timeline, full traceability.

Structured delivery graph

Every commit, review, deploy, and conversation becomes a queryable node. No more tab-switching to reconstruct what happened.

Org-wide rollup

Aggregate delivery signals across teams and repos. See patterns at the org level that are invisible in individual tools.

Spec-scoping flags

Detect Linear and Jira tasks missing acceptance criteria, clear scope, or linked design docs. Flag under-specified work before coding starts — so agents and engineers build to well-defined specs.

Review intelligence

Correlated delivery data reveals where reviews stall and why. Pair with Agentic Checks to auto-triage based on risk.

Review queue depth

Track how many PRs each engineer has pending. Surface overloaded reviewers and redistribute work before bottlenecks form.

Bottleneck detection

Identify which teams, repos, or individuals consistently slow down review cycles. Correlate with Slack activity and meeting load.

Review velocity trends

Track review turnaround over time across teams. Spot regressions early and measure the impact of process changes.

Structured insights from correlated data

Once your delivery data is unified, Warestack surfaces KPIs automatically — no configuration required.

CODE REVIEW↗ +8%

PRs Merged Without Review

CODE REVIEW↘ -12%

Review Turnaround Time

4.2h

CODE REVIEW↗ +3

Stale & Aging PRs

DEPLOYMENT↘ +15%

Deployment Frequency (DORA)

3.2/day

DEPLOYMENT↘ -22%

Failed Deploys & Rollbacks

DEPLOYMENT↘ -18%

Lead Time for Changes

2.1 days

AGENT INTELLIGENCE↘ +19%

Agent Co-Authorship Rate

38.8%

Each KPI includes trend tracking and period-over-period comparison. Query them in natural language →

Frequently asked questions

All events are transformed into Warestack's canonical schema — a structured model that maps PullRequest, Review, Commit, Issue, and WorkflowRun into SQL tables with deterministic joins. No custom configuration required — the schema is consistent across every connected repo and tool.

Warestack continuously captures GitHub PR metadata, CI/CD logs, Slack discussions, and project management updates from Linear and Jira. Data is available as raw event streams (JSON/Webhooks) or directly in Warestack's unified dashboard — no manual export needed.

Every data object is enriched with computed fields beyond what the raw event provides. Examples include review_latency, loc_changed, is_afterhours, and risk_score. ML models run over historical patterns to detect anomalies and surface signals that plain event data would miss.

Warestack maintains a shared reference graph between entities using temporal and semantic keys — PR IDs, commit hashes, message references. This lets you trace relationships between a PR, failed_deploy events, and rushed_merge patterns — or link a Slack decision back to the ticket that specified it and the PR that shipped it.

Yes. Warestack translates plain English prompts into structured queries over the delivery graph. Ask "Which PRs were merged without full review compliance this week?" and get a structured, timestamped answer — no SQL required. You can also schedule recurring queries and receive results via Slack or email.