Intelligent Documentation Database Modernization
Role: UX & AI Research Lead | Client: Multi-Site Logistics Operations
Brief:
Transformed a fragmented, manual document management process into an AI-enabled centralized documentation database that provides real-time search, version control, and compliance tracking. Designed to support operational consistency across geographically distributed warehouses, the system integrates governance standards for data accuracy, role-based access, and regulatory compliance.
Product Framing & Success Criteria
Product Goal: Improve operational consistency and compliance by replacing fragmented document storage with a centralized, AI-enabled documentation platform that supports fast retrieval, governance, and auditability.
Primary Users: Warehouse staff accessing procedures, supervisors managing documentation updates, and compliance or quality teams responsible for audits and regulatory readiness.
Business Constraints: Distributed sites with inconsistent documentation practices, regulatory and safety requirements, access control needs, and low tolerance for outdated or inaccurate information.
Success Metrics: Document retrieval time, search accuracy, user adoption across sites, audit readiness, and compliance coverage.
Key Objectives
Consolidate scattered documents into a centralized, AI-searchable repository.
Reduce time-to-locate critical documents for operational and compliance purposes.
Implement AI-driven metadata tagging to improve discoverability and reduce human error in filing.
Establish governance protocols for version history, audit logs, and access control.
Process & Responsibilities
Conducted stakeholder interviews across multiple warehouse locations to identify critical documentation pain points.
Collaborated with data engineering teams to integrate AI-enabled OCR (Optical Character Recognition) and natural language search capabilities.
Designed role-based access structures to meet governance and compliance requirements (including ISO and industry safety standards).
Led usability testing with warehouse staff to ensure the database UI supported rapid search and intuitive navigation.
Implemented change management strategy to drive adoption across all operational sites.
Key Product Decisions & Tradeoffs
Centralization vs. Local Autonomy: Centralized documentation storage while allowing controlled site-level contributions to maintain consistency without blocking local needs.
AI Tagging vs. Manual Classification: Used AI-driven OCR and metadata tagging to reduce human error while retaining manual review for high-risk documents.
Search Power vs. Simplicity: Prioritized fast, intuitive search and filtering over more complex knowledge graph features to support adoption by non-technical users.
Governance Rigor vs. Usability: Balanced strict access controls and audit logging with a streamlined user experience to avoid compliance workarounds.
Outcome
Reduced average document retrieval time from 15 minutes to under 30 seconds.
Achieved full traceability with automated version history and governance-compliant audit logs.
Increased document search accuracy by 92% through AI-driven tagging and classification.
Enhanced operational compliance with a centralized, verifiable repository.
