Janet AI automates Jira ticket triage and management by analyzing issues to identify relationships, categorize them, and surface contextual intelligence. This AI-native layer helps software teams quickly understand problem scope and impact, reducing manual overhead and improving issue resolution.
Funding
Funding not disclosed
Founders
Product
Problem
Software development teams using Jira face significant overhead in manually triaging, categorizing, and linking issues, which diverts valuable engineering time from core development tasks. This manual process often leads to delays in identifying critical bugs and understanding the root cause of recurring problems.
Solution
Janet AI provides an AI-native layer that integrates directly with Jira to automate ticket triage and management. The platform continuously analyzes incoming Jira tickets, automatically identifying relationships between issues, categorizing them by type, and surfacing contextual intelligence. This enables development teams to quickly understand the scope and impact of problems, such as linking duplicate bugs or identifying shared root causes. Janet AI's conversational interface allows users to query for specific issues, trigger Jira actions like linking tickets or adding comments, and receive insights into project-wide trends and critical blockers.
Target Audience
The primary users are software engineering teams, DevOps professionals, and project managers who utilize Jira for issue tracking and project management.
Features
- AI-driven ticket analysis for automated triage, auto-discovery of related issues, and auto-linking of duplicates.
- Contextual intelligence engine that identifies shared root causes and symptoms across disparate tickets.
- Natural language processing (NLP) interface for querying Jira data and performing ticket management actions via chat.
- Automated categorization of tickets based on issue type and severity.
- Integration with Jira via OAuth 2.0 and support for Enterprise Single Sign-On (SSO).
- End-to-end encryption for data in transit and at rest, with fully siloed customer data.
- Commitment to LLM security, ensuring customer data is not used for model training and supporting private model processing for enterprise deployments.
- SOC 2 and GDPR compliance.