Javis
AI bridge connecting Jira and development workflows
The Problem
Jira/Confluence Data Silos
Jira stories, Confluence docs, and local development context live in completely separate systems. Developers constantly alt-tab between tools to cross-reference requirements, specifications, and code — losing context at every switch.
Manual Sprint Tracking
Sprint progress, velocity metrics, and risk indicators are compiled manually from Jira data. By the time a status report is assembled, it's already outdated. Sprint retrospectives lack data-driven insights.
No Dev Context in Jira
Jira tickets lack technical context — implementation notes, architectural decisions, and dependency information live in developers' heads or scattered across chat messages. New team members struggle to understand the full picture behind each story.
Alt-tabbing between Jira, Confluence, and IDE to cross-reference
Bidirectional sync keeps Jira, Confluence, and local DB in sync
Manually compiling sprint metrics from Jira exports
AI-powered sprint dashboard with risk analysis and velocity trends
Technical context lost in chat threads and developer memory
Structured story management with dev context synced back to Jira
Approach
Local-First AI Project Copilot
Javis bridges Jira/Confluence and local development by maintaining a synchronized local SQLite database that mirrors project data from both platforms. The system provides 8 Claude Code skills (init, story, dev, report, risk, sprint, sync, deploy) that developers invoke from their terminal. AI-powered risk detection monitors 5 risk types (delay, blocker, velocity drop, dependency, resource) using sprint and story data. Bidirectional incremental sync ensures changes flow both ways — updates made locally propagate to Jira, and Jira changes sync back to the local DB. Sprint dashboards, story management, and Confluence report generation all work from the local-first data store, keeping developers in their IDE while staying connected to the broader project management ecosystem. Slack integration provides outbound alerts for risk notifications and inbound commands for quick status checks.
BDD Pipeline Flow
Key Features
Sprint Dashboard
Real-time sprint overview with velocity trends, burndown charts, and completion forecasting. Aggregates data from Jira sprints with local development context to provide a complete picture of sprint health without leaving the terminal.
Story Management
Create, update, and enrich Jira stories with technical context from the development environment. Implementation notes, architectural decisions, and dependency mappings are captured locally and synced back to Jira, preserving dev knowledge in the project management layer.
Risk Analysis
AI monitors 5 risk types across the sprint: delay risks (stories behind schedule), blockers (unresolved dependencies), velocity drops (declining throughput), dependency risks (cross-team bottlenecks), and resource risks (overallocated team members). Alerts are sent via Slack when risk thresholds are exceeded.
Data Sync (Jira ↔ Local DB)
Bidirectional incremental sync between Jira and the local SQLite database. Only changed records are transferred using JQL-based delta queries and local change tracking. Confluence pages are also synced for documentation context, creating a unified local-first data layer.
Architecture
Results
init, story, dev, report, risk, sprint, sync, deploy
Delay, blocker, velocity, dependency, resource
Bidirectional incremental sync with Jira & Confluence
SQLite local-first with cloud sync on demand