Mail-to-Jira Orchestrator
Turn inbound emails into clean Jira tickets with accurate subtasks
Trusted by
Uses deterministic JSON schemas, Jira Cloud REST APIs, and a proven LLM (Llama 3.2) for robust, repeatable ticket creation
Success Story
Hoag Health cut ticket resolution time by 86% and raised CSAT by +13 pts after moving from Outlook
Integrates with
Problem
Service teams receive work via email, but manual triage is slow and error-prone. Requests lack structure, important details are missed, and creating consistent Jira tickets steals time from actual resolution
Solution
The agent reads each email, classifies type and priority, generates a concise summary, and extracts subtasks in JSON. It then creates the main Jira issue plus linked subtasks—complete with descriptions and assignees—so engineers can begin immediately
Result
Faster first response and resolution, fewer mis-routed tickets, standardized descriptions, and measurable reduction in manual triage effort
Use Cases
Mail-to-Jira Orchestrator streamlines service intake by reading new emails, understanding intent, and creating ready-to-work Jira tickets with linked subtasks. The agent classifies requests (bug/feature/support), summarizes context, extracts action items, and assigns the right project/issue type. It reduces triage effort, standardizes descriptions, and prevents lost requests buried in inboxes. The workflow uses Llama 3.2 for natural-language understanding and follows strict JSON schemas to ensure predictable, parseable outputs. Teams get faster first responses, fewer routing errors, and consistently formatted tickets their engineers can start on immediately
Integrations
Connect to your existing tools seamlessly
Technology Stack
Automation
Automation
Infrastructure
Implementation Timeline
Gmail Filter & Polling Setup
~1 dayConfigure Gmail filters to capture relevant inbound emails and enable secure polling or webhook triggers for message intake
Project & Template Mapping
~1–2 daysDefine Jira project mappings, issue types, and ticket templates to standardize summaries, priorities, and ownership
Prompt & Schema Calibration
~2–3 daysFine-tune Llama 3.2 prompts for accurate intent detection and validate JSON schema outputs for predictable, structured ticket data
Jira Cloud Integration
~2–3 daysConnect Jira Cloud via API, authenticate securely, and configure field mappings for automated issue and subtask creation
QA & Routing Refinement
~2–4 daysRun pilot tests with real sample emails, verify ticket accuracy, and adjust classification and routing logic based on QA feedback
Rollout & Monitoring
~2–6 daysDeploy to production, enable monitoring dashboards, audit logs, and error notifications to ensure stable, compliant operation
Support Included
Setup guide, prompt templates, JSON schema samples, field-mapping checklist, rollback & safety playbook
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