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Case study
Internal AI Tooling Suite
Custom MCP server integrating LLM automation into Jira and Confluence workflows.
Built an MVP internal AI tooling suite that brings LLM-powered automation to regulated financial workflows.
Impact
45% faster Jira ticket resolution
Impact
62% reduction in documentation time
Impact
8 business lines adopted in 3 months
Impact
Zero data leakage in regulated environment
Problem
Knowledge workers across the enterprise spent hours creating, updating, and searching for documentation in Jira and Confluence, with inconsistent quality and discoverability.
Architecture
AI Tooling Architecture
Select a component to explore its role and connections.
User
Engineers, Product, PMs
Connects to: LLM Gateway
Approach
- Designed a custom MCP server exposing Jira and Confluence APIs as LLM tools.
- Implemented scoped access controls ensuring data never leaves regulated boundaries.
- Created specialized prompts for ticket summarization, documentation generation, and search augmentation.
- Deployed within enterprise guardrails with audit logging and approval workflows.
Outcomes
- Teams use AI to draft tickets, generate release notes, and create runbooks faster.
- Knowledge is more discoverable with semantic search across Jira tickets.
- Reduced cognitive load on engineers during release management cycles.
- Security team approved deployment within regulated financial environment.
Metrics
Before → After comparison
Before
- ticket Creation Time
- 18 minutes
- doc Creation Time
- 45 minutes
- search Success
- 58%
- weekly Ai Tickets
- 0
After
- ticket Creation Time
- 8 minutes
- doc Creation Time
- 17 minutes
- search Success
- 84%
- weekly Ai Tickets
- 340+
Stack
TypeScriptMCP (Model Context Protocol)Jira APIConfluence APILangChain