Client Context
A global technology services company supporting several enterprise products relied on Confluence, SharePoint, Jira, Git repositories, and service-desk systems to support incident resolution, release support, and technical knowledge discovery. The operating model handled thousands of documents and support records across North America, Europe, and India, using product documentation, runbooks, defects, and support histories. Support engineers spent too much time locating the latest approved answer. Product permissions and outdated documents made unrestricted retrieval unacceptable.
Challenges
- Support engineers spent too much time locating the latest approved answer.
- Product permissions and outdated documents made unrestricted retrieval unacceptable.
- Business knowledge was distributed across Confluence, SharePoint, Jira, Git repositories, and service-desk systems, making reliable retrieval slow and inconsistent.
- Generic AI responses were not grounded in approved product documentation, runbooks, defects, and support histories, creating accuracy and audit concerns.
- Access controls, sensitive-data handling, and regional governance requirements varied across North America, Europe, and India.
Solutions Implemented
- Implemented role-aware retrieval and document freshness scoring.
- Added expert feedback capture to improve retrieval and answer evaluation.
- Designed a secure retrieval-augmented generation architecture aligned to Confluence, SharePoint, Jira, Git repositories, and service-desk systems and existing identity controls.
- Built ingestion pipelines to classify, clean, chunk, enrich, and index product documentation, runbooks, defects, and support histories with traceable metadata.
- Implemented grounded-response controls, source citations, confidence thresholds, and human escalation paths.
- Created automated evaluation suites covering relevance, factual consistency, safety, response time, and regression drift.
Value Delivered
Reduced quote approval testing time by 65%.
Minimized API sync issues through automated validation and alerting.
Enhanced overall test coverage across CPQ, integration, and pricing workflows.
Enabled early defect detection and faster UAT sign-off.
Impact Highlights
- Achieved an estimated [35%] improvement in cycle time, coverage, or processing consistency; replace with the approved client metric.
- Reduced relevant defects, failures, or rework by an illustrative [25%]; confirm before publication.
- A production-ready knowledge platform for support and engineering teams.
- Created measurable controls for AI quality, cost, and risk.
