Value From Day One
How AI automatically classifies your engineering work without manual tagging or configuration overhead.
The Setup Overhead Problem
Most engineering tools die in the configuration phase
Why Traditional Tools Fail:
Manual Categorization Required
Teams must tag tickets, classify work types, and maintain custom taxonomies — work that's immediately outdated.
Complex Integration Setup
Weeks of configuration across Git, JIRA, Slack, and CI/CD tools before seeing any value.
No Historical Analysis
Can only analyze work done after setup—losing months or years of valuable pattern data.
Maintenance Overhead
Categories drift, integrations break, and someone needs to manage the engineering metrics system.
How AI Auto-Classification Works
Technical deep dive into zero-config analysis
1. Git History Analysis
AI analyzes your existing Git history to automatically classify work patterns:
• File Path Patterns: `/tests/` = testing work, `/docs/` = documentation
• Commit Message Analysis: "fix", "bug", "hotfix" = defect resolution
• Change Patterns: Large deletions = refactoring, new files = feature development
• Touched Components: Database migrations, API changes, UI updates
2. Workflow Pattern Recognition
AI identifies team workflows from actual behavior, not configuration:
• Branch Patterns: feature/, hotfix/, release/ naming conventions
• Review Cycles: How code moves from draft to approval to merge
• Deployment Patterns: Which commits trigger deployments, rollback behavior
• Collaboration Patterns: Who reviews what, knowledge sharing behaviors
3. Semantic Code Analysis
Deep analysis of code changes to understand intent and impact:
• AST Analysis: Understanding code structure changes, not just line diffs
• Dependency Tracking: Which changes affect which parts of the system
• Test Coverage Impact: How changes affect test coverage and risk
• Performance Implications: Database queries, API calls, algorithm changes
4. Contextual Classification
AI combines multiple signals for accurate work classification:
• Time Context: Rush fixes vs. planned work, end-of-sprint patterns
• Team Context: Author expertise, collaboration patterns, review behaviors
• Project Context: Feature flags, A/B tests, infrastructure changes
• Business Context: Release cycles, incident response, technical debt paydown
Auto-Classification Examples
Real commits and how AI categorizes them
Example 1: Authentication Refactor
Commit: "Migrate user auth from JWT to OAuth2, update middleware"
Files: auth/jwt.ts (deleted), auth/oauth.ts (new), middleware/auth.ts (modified)
AI Classification: Security Enhancement + Technical Debt Reduction
Risk Level: High (authentication system changes)
Effort Type: Refactoring (existing functionality, improved security)
Knowledge Area: Authentication, Security, Backend Infrastructure
Example 2: UI Bug Fix
Commit: "Fix dropdown menu positioning on mobile Safari"
Files: components/Dropdown.tsx, styles/mobile.css
Time: 2:30 AM Friday
AI Classification: Defect Resolution + Cross-Browser Compatibility
Risk Level: Low (UI-only changes)
Urgency: High (weekend emergency fix pattern)
Knowledge Area: Frontend, Mobile, CSS, Browser Compatibility
Example 3: Database Performance
Commit: "Add indexes to user_sessions table, optimize auth queries"
Files: migrations/add_session_indexes.sql, queries/auth.sql
Impact: 40% of existing code affected
AI Classification: Performance Optimization + Database Maintenance
Risk Level: Medium (database schema changes)
Impact Scope: System-wide (authentication affects all features)
Knowledge Area: Database, Performance, Backend Infrastructure
Technical Integration
How zero-config works with your existing tools
Read-Only Git Integration
No webhooks, no write permissions required. AI analyzes your Git history without changing anything or requiring special access.
Instant Historical Analysis
Get insights from months or years of existing work immediately—no waiting to accumulate new data.
Self-Improving Classification
AI learns your team's patterns and improves accuracy over time without requiring training data or manual correction.
Privacy-First Design
Code content stays private—AI analyzes metadata, structure, and patterns without accessing sensitive business logic.