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  • CASE STUDY | SEMICONDUCTOR / HUMAN INTERFACE SOLUTIONS

Enterprise AI activated. Cross-system search live in a single engagement.

A global semiconductor company partnered with Trundl to activate the full value of its Jira Enterprise investment. With data spread across Jira, Confluence, SharePoint, and Miro, the organization deployed Atlassian Rovo and built a custom SharePoint ingestion pipeline to unify search across all sources. Analytics dashboards gave PMO and IT leadership real-time visibility for the first time.

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4 automation flows built
3+ data sources unified
90-day adoption analytics
1 search layer
  • ON THIS PAGE
Industry Semiconductor / Human Interface Solutions
Solution Atlassian Rovo + Analytics + SharePoint Ingestion
Model Fixed Engineering Hours
Partner Tier Atlassian Platinum Solution Partner
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  • THE CLIENT

Who is the client?

A global technology company specializing in touch, display, and biometric sensing products operates a high-scale Jira Enterprise environment. With critical information distributed across Jira, Confluence, SharePoint, and external sources, the organization needed a way to surface the right data at the point of decision, without switching tools.

  • THE BUSINESS CHALLENGE

Data scattered. Search insufficient. AI not yet ready.

01

Data Fragmentation

Critical information lived across Jira, Confluence, SharePoint, and Miro with no unified search layer connecting them.

02

Environment Readiness

The existing Jira instance needed cleanup before AI tools could return accurate, high-quality results.

03

Tooling Clarity

The team needed clear boundaries between general enterprise AI (ChatGPT) and context-aware AI (Atlassian Rovo) to drive adoption.

04

Search Limitations

Technical documentation stored in PDF format inside SharePoint was not surfaced by any existing search tool.

  • PROJECT OBJECTIVES

From data storage to active intelligence.

01

Rovo Proof of Concept

Stand up Atlassian Rovo across the active Jira and Confluence environment.

02

Enterprise Search

Connect Jira, Confluence, SharePoint, and Miro into a single AI-searchable knowledge layer.

03

Analytics Foundation

Build Atlassian Analytics dashboards for PMO and IT leadership

04

Environment Quality

Remediate data quality issues to ensure AI responses draw from accurate, current information.

  • THE TRUNDL SOLUTION

A strategic AI and analytics framework.

01

Environment Remediation

Conducted a comprehensive Jira cleanup addressing orphaned data and refining configurations to give Rovo a clean, accurate source to index.

02

SharePoint Ingestion Pipeline

Built four distinct Power Automate flows: weekly PDF exports of SharePoint pages, event-driven PDF recreation on content updates, orphan PDF cleanup, and Rovo-optimized text-only PDFs with embedded source URLs for AI indexing.

03

Atlassian Analytics Dashboards

Deployed dashboards tracking Rovo and Confluence adoption over 90-day windows, license utilization, and project health metrics for PMO and IT leadership.

04

AI Policy and Training

Delivered a comparative analysis of Rovo versus enterprise ChatGPT, hands-on user training sessions, and governance guidance for third-party connectors within AI search results.

  • DELIVERY HIGHLIGHTS

Quality, value, and speed.

QUALITY Day One Accuracy

Environment cleanup ensured Rovo returned high-confidence answers against the organization's actual knowledge base, not stale or orphaned data.

TIME TO MARKET Fixed Hours

The flexible engagement model let the team resolve immediate data quality blockers first, then build toward the full AI and analytics layer.

VALUE ADD Unified Search

Jira, Confluence, SharePoint PDFs, and Miro content unified in a single AI-searchable layer, accessible without switching tools.

  • FAQ

Frequently asked questions.

Why was environment cleanup necessary before Rovo deployment?

AI search quality reflects the quality of the content it indexes. Orphaned projects, duplicate pages, and inconsistent configurations produce noise. Trundl resolved these first so the team’s first Rovo experience returned accurate results.

How does the SharePoint ingestion pipeline work?

Four Power Automate flows handle scheduled and event-driven sync, converting SharePoint pages to PDFs with embedded source URLs for Rovo indexing. Cleanup automations maintain index hygiene as content changes over time.

How were Rovo and ChatGPT positioned relative to each other?

Trundl delivered a comparative analysis defining clear use-case boundaries: Rovo for context-aware Atlassian searches against internal data; enterprise ChatGPT for general reasoning tasks. Both remain available. Overlap is eliminated by policy, not restriction.