344 lines
14 KiB
Markdown
344 lines
14 KiB
Markdown
# JIRA AI Fixer
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## Executive Proposal
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**Date:** February 2026
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**Version:** 1.1
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**Classification:** Product Documentation
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---
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## Executive Summary
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### The Problem
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Support teams face growing challenges in resolving Support Cases:
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| Challenge | Impact |
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|-----------|--------|
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| **Response time** | Initial analysis consumes hours of senior developer time |
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| **Growing backlog** | Issues accumulate while team focuses on urgent demands |
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| **Variable quality** | Dependency on individual knowledge about the code |
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| **Concentrated knowledge** | Few specialists know all modules |
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### The Solution
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An **Artificial Intelligence** system that:
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1. **Monitors** new Support Cases in JIRA automatically
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2. **Analyzes** the problem and identifies affected source code
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3. **Proposes** specific fixes in COBOL, SQL, and JCL
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4. **Documents** the analysis directly in JIRA
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5. **Creates** branches with fixes for human review
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### Expected Result
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```
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ BEFORE vs AFTER │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ BEFORE AFTER │
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│ ────── ───── │
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│ Issue created Issue created │
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│ ↓ ↓ │
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│ Dev analyzes (2-4h) AI analyzes (5min) │
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│ ↓ ↓ │
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│ Search code (1-2h) Code identified │
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│ ↓ ↓ │
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│ Investigate cause (2-4h) Cause + suggested fix │
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│ ↓ ↓ │
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│ Develop fix (2-4h) Dev reviews and approves │
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│ ↓ ↓ │
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│ Review + deploy Review + deploy │
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│ │
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│ TOTAL: 8-14 hours TOTAL: 2-4 hours │
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│ │
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│ ✅ 60-70% reduction in resolution time │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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```
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---
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## Why Now?
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### 1. Mature Technology
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Language models (GPT-4, Claude, Llama) have reached sufficient quality for code analysis and generation, including legacy languages like COBOL.
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### 2. Competitive Advantage
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Leading companies are adopting AI to accelerate development. Those who don't adopt will fall behind in productivity.
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### 3. Manageable Volume
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With typical support volumes, the risk is low and the environment is ideal to validate the solution before scaling.
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### 4. Accessible Cost
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Operational cost is minimal, especially with free/low-cost LLM options available.
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---
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## How It Works
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### Simplified Flow
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```
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┌──────────────┐ ┌──────────────┐ ┌──────────────┐
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│ Support │ │ AI │ │ Dev │
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│ Case │────▶│ Analyzes │────▶│ Reviews │
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│ (JIRA) │ │ + Suggests │ │ + Approves │
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└──────────────┘ └──────────────┘ └──────────────┘
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5min 5min 30min-2h
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┌─────────────────────┐
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│ JIRA Comment: │
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│ - Root cause │
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│ - Affected files │
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│ - Proposed fix │
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│ - Link to PR │
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└─────────────────────┘
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```
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### Real Example
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**Issue:** "Transaction being declined with code 51 even with available balance"
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**AI Response (in 5 minutes):**
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```
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📋 AUTOMATIC ANALYSIS
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🔍 Identified Cause:
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The AUTH.CBL program is comparing the WS-AVAILABLE-BALANCE field
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with format PIC 9(9)V99, but the value returned from HOST uses
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PIC 9(11)V99, causing truncation.
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📁 Affected File:
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- src/cobol/AUTH.CBL (lines 1234-1256)
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💡 Proposed Fix:
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Change WS-AVAILABLE-BALANCE declaration to PIC 9(11)V99
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and adjust the comparison in SECTION 3000-VALIDATE.
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📊 Confidence: 87%
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🔗 PR with fix: bitbucket.example.com/projects/PRODUCT/repos/...
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```
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### Security: AI Does Not Alter Production Code
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```
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ SEPARATION OF RESPONSIBILITIES │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ CLIENT Repository (production) │
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│ Product-Client-Fork │
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│ ├── AI has access: READ ONLY │
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│ └── Changes: ONLY by developers │
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│ │
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│ AI Repository (isolated) │
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│ Product-Client-AI │
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│ ├── AI has access: READ AND WRITE │
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│ └── Purpose: Branches with fix suggestions │
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│ │
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│ Approval Flow: │
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│ 1. AI creates branch in isolated repository │
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│ 2. AI opens Pull Request to client repository │
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│ 3. HUMAN developer reviews │
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│ 4. HUMAN developer approves or rejects │
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│ 5. Only then code goes to production │
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│ │
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│ ✅ 100% of changes go through human review │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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```
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---
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## Investment
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### Pricing Models
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| Model | Description | Price |
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|-------|-------------|-------|
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| **SaaS** | Hosted, managed by vendor | $2,000 - $5,000/month |
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| **On-Premise License** | Self-hosted, perpetual | $50,000 - $100,000 one-time |
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| **Enterprise** | Custom deployment + support | Contact for quote |
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### ROI Calculation
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```
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Senior developer hourly cost: ~$40-80
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Average time saved per issue: 6-10 hours
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Monthly savings (10 issues): $2,400 - $8,000
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SaaS payback: Immediate positive ROI
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Enterprise license payback: 12-24 months
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```
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### Intangible Benefits
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| Benefit | Impact |
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|---------|--------|
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| **Standardization** | All issues analyzed with same rigor |
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| **Documentation** | Complete analysis history in JIRA |
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| **Knowledge** | AI learns patterns, doesn't depend on people |
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| **Speed** | Initial response in minutes, not hours |
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| **Team morale** | Devs focus on complex problems, not repetitive ones |
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---
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## Deployment Options
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### Option 1: SaaS (Recommended for Quick Start)
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```
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✅ Fastest time-to-value (days, not months)
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✅ No infrastructure to manage
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✅ Automatic updates
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✅ Included support
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```
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### Option 2: On-Premise (For Compliance Requirements)
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```
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✅ 100% data stays in your infrastructure
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✅ Air-gapped option (no internet required)
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✅ Full control over updates
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✅ One-time license cost
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```
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### Option 3: Hybrid
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```
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✅ You host, we manage
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✅ Balance of control and convenience
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✅ Flexible pricing
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```
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---
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## Security and Compliance
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### LLM Provider Options
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| Provider | Data Location | Compliance Level |
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|----------|---------------|------------------|
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| **Azure OpenAI** | Your Azure tenant | Enterprise |
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| **Local (Ollama)** | Your servers | Air-gapped |
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| **OpenAI API** | OpenAI cloud | Standard |
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| **OpenRouter** | Various | Development |
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### Compliance Features
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- ✅ Data segregation by client/product
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- ✅ Complete audit trail
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- ✅ Configurable log retention
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- ✅ 100% on-premise deployment option
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- ✅ Air-gapped deployment available
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- ✅ No code sent to public training datasets
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---
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## Risks and Mitigations
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| Risk | Probability | Mitigation |
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|------|-------------|------------|
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| **AI suggests incorrect fix** | Medium | Mandatory human review in 100% of cases |
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| **Team resistance** | Low | Position as assistant, not replacement |
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| **Code security** | Configurable | Choose Azure/local for compliance |
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| **LLM cost increases** | Low | Multiple provider options |
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### Conservative Approach
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The system is designed for phased adoption:
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```
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Phase 1: Analysis and suggestion only
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AI comments in JIRA, doesn't create code
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Phase 2: Code generation in isolated repository
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Human decides whether to use or not
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Phase 3: Automatic Pull Requests
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Human still approves
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Phase 4: Auto-merge (only for high-confidence fixes)
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Only after months of validation
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```
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---
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## Implementation Timeline
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```
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ IMPLEMENTATION ROADMAP │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ Week 1-2 Week 3-4 Week 5-6 Week 7+ │
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│ ──────── ──────── ──────── ──────── │
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│ Setup + Code Business Go-Live + │
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│ Integrations Indexing Rules Refinement │
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│ │
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│ ✓ JIRA ✓ COBOL ✓ Modules ✓ Production │
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│ ✓ Bitbucket ✓ SQL ✓ Validation ✓ Adjustments │
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│ ✓ Portal ✓ JCL ✓ Testing ✓ Support │
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│ │
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│ │ │
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│ ▼ │
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│ LIVE │
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│ ~5-7 weeks │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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```
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---
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## Solution Differentiators
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### Why JIRA AI Fixer?
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| Aspect | Generic Tools | JIRA AI Fixer |
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|--------|---------------|---------------|
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| **JIRA Integration** | ❌ Manual | ✅ Automatic |
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| **Domain knowledge** | ❌ Generic | ✅ Configurable business rules |
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| **COBOL expertise** | ⚠️ Limited | ✅ Optimized for mainframe |
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| **Support Case flow** | ❌ Doesn't exist | ✅ Native |
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| **Deployment options** | ❌ Cloud only | ✅ SaaS, on-prem, or air-gapped |
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| **Customization** | ❌ Generic | ✅ Fully configurable |
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---
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## Next Steps
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### To Get Started
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1. **Schedule Demo** - See JIRA AI Fixer in action with your data
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2. **Pilot Program** - 30-day trial with limited scope
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3. **Full Deployment** - Production rollout with support
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### Contact
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- **Email:** sales@yourcompany.com
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- **Demo Request:** https://jira-ai-fixer.yourcompany.com/demo
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---
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## Conclusion
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**JIRA AI Fixer** represents an opportunity to:
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✅ **Increase productivity** of support team by 60%+
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✅ **Reduce response time** from hours to minutes
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✅ **Standardize quality** of analyses
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✅ **Retain knowledge** independent of people
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✅ **Choose your deployment** - SaaS, on-prem, or air-gapped
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The timing is ideal: mature technology, flexible deployment options, and proven ROI.
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---
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**JIRA AI Fixer - Intelligent Support Case Resolution**
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*Ready to transform your support workflow?*
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