Abstract

An AI-powered unified content discovery system that enables employees to find relevant information across The Source, Confluence, Jira, Slack, Google Drive, and Developer Hub through natural language search with AI-generated summaries, contextual ranking, and content quality indicators. The system synthesizes information from multiple sources into coherent summaries with source references, eliminates information silos, reduces search time by 50%, and decreases documentation duplication by 40%. Phase 1 indexes only publicly available internal content, with architecture designed to support role-based access controls (e.g., manager-only content) in future releases.

Description

The organization's knowledge assets are fragmented across 6+ disconnected systems, forcing employees to waste time manually searching multiple platforms, often recreating content they cannot find, and struggling to identify authoritative or current sources. This AI-powered content discovery system provides a single intelligent interface that indexes all publicly available internal content, understands natural language queries, generates AI summaries synthesizing information from multiple sources, and applies contextual ranking.

The system delivers AI-generated summaries with inline citations and source references, unified search results ranked by relevance and recency, freshness and conflict indicators, and cross-system content linking. It provides a dramatically superior discovery experience that reduces cognitive load and ensures employees work with synthesized, comprehensive information rather than piecing together knowledge from scattered documents.

Key capabilities include natural language query processing, AI-powered summary generation with source citations, multi-source indexing of publicly available content, contextual ranking, content quality signals (freshness, conflicts), and analytics to continuously improve relevance. Phase 1 focuses on publicly available internal content only, with system architecture designed to support role-based access controls for restricted content in future releases.

Environment

Corporate content is scattered across The Source (internal knowledge base), Confluence spaces (project wikis and documentation), Jira projects (tickets and requirements), Slack channels (discussions and tribal knowledge), Google Drive (majority of documents), and Developer Hub (technical documentation). Each system has different search capabilities, access controls, and user interfaces. Content is often behind VPN or internal network access. Search quality varies significantly: some systems support only keyword matching, while others have limited relevance ranking. Users must maintain mental models of which content types live in which systems and develop system-specific search strategies.

Existing access control policies across these systems are complex and must be preserved. Corporate data residency requirements mandate that indexed content and search logs remain within approved regions. SSO integration is required for authentication. Network security policies include firewall traversal, proxy configuration, and certificate validation that constrain integration approaches.

Goals & Objectives

Enable employees to find relevant content from any source system in under 2 minutes through intelligent unified search. Reduce documentation duplication by 40% by surfacing existing materials before new content is created. Improve employee satisfaction with knowledge management tools by 30 percentage points. Reduce support burden related to "can't find documentation" by 60%. Achieve 75% employee adoption within 3 months of launch.

Measurable Outcomes:

• SC.001: 80% of information needs resolved within 2 minutes
• SC.003: 40% reduction in duplicate documentation creation
• SC.005: 50% employee adoption rate within 90 days
• SC.006: 50% increase in cross-system content discovery

Key Features

• KF.001: AI-powered summary generation synthesizing information from multiple sources with inline citations and clickable source references
• KF.002: Unified natural language search across The Source, Confluence, Jira, Slack, Google Drive, and Developer Hub
• KF.003: Semantic search with relevance ranking by content match, recency, and user context
• KF.004: Toggle between AI summary view and traditional document list view
• KF.005: Content quality indicators (freshness status, conflict detection, usage metrics)
• KF.006: Cross-system content linking showing related materials from different sources
• KF.007: Indexing of publicly available internal content only (Phase 1), with architecture supporting future role-based access for restricted content
• KF.008: SSO integration for user authentication
• KF.009: Near-real-time index updates as source content changes (12-hour maximum staleness)
• KF.010: User-driven content quality feedback (report outdated content, rate results)
• KF.011: Search analytics and continuous learning from user behavior

Key Entities

• Content Item: Indexed document/page/thread from publicly available internal sources with metadata (title, author, dates, source, freshness status)
• AI Summary: Generated summary synthesizing multiple content items with inline citations and source references
• User Profile: Employee identity with search history, clicked results, and preferences
• Search Query: User search request with filters, AI summary, referenced documents, and engagement metrics
• Content Source: Connected system (Confluence, Google Drive, etc.) with health status and indexing schedule
• Content Relationship: Semantic links between items (references, duplicates, conflicts, supersedes)

Non-Goals (for this Epic)

• Indexing restricted/permission-based content: Phase 1 only indexes publicly available internal content accessible to all employees. Role-based access for manager-only or department-specific content is out of scope (architecture supports future implementation)
• Building a full content management system with editing, version control, or approval workflows
• Authoring or modifying documents within the discovery interface (users navigate to source systems for editing)
• Organizational policy enforcement or compliance workflow automation beyond access control inheritance
• Replacing existing source systems, this is a discovery layer, not a replacement
• Automatic content deduplication or merging (system flags conflicts but requires human resolution)
• Translating content between languages (system indexes content in original language)
• Email or slack integration (focus on persistent content repositories, not ephemeral communication)

Dependencies / Open Questions

Dependencies:

• SSO integration for user authentication and identity
• API access or integration credentials for each source system (Confluence, Jira, Google Drive, Slack, The Source, Developer Hub)
• Content extraction capabilities for each source (some may require connectors or custom parsers)
• Network connectivity to access systems behind VPN/internal network
• Hosting infrastructure with appropriate data residency compliance
• AI/ML capabilities for semantic search and AI summary generation (vendor solution or in-house platform decision required)
• Large Language Model (LLM) access for generating summaries with citations from multiple source documents

Open Questions:

• What AI/ML platform or vendor will provide semantic search and summary generation capabilities?
• When should role-based access controls for restricted content be implemented (Phase 2, Phase 3)? What are the specific use cases (manager-only content, department-specific, team-specific)?

Deliverables

• Functional AI-powered content discovery system accessible via web interface
• Integration connectors for all 6+ source systems
• User documentation and training materials
• Administrator guide for configuring sources and freshness thresholds
• Analytics dashboard for search metrics, adoption tracking, and content quality monitoring
• Migration and rollout plan with phased deployment strategy
• Post-launch support plan and continuous improvement roadmap

Related Links

• Specification Document
• Implementation Plan
• Task Breakdown

Notes

This specification is implementation-agnostic and does not prescribe specific technologies, frameworks, or AI platforms. Implementation teams should evaluate options (build vs. buy, cloud vs. on-premise, specific AI vendors, LLM providers) during the planning phase based on organizational constraints, existing infrastructure, and total cost of ownership.

Phase 1 Scope: This initial release focuses on publicly available internal content only. The system architecture is designed with extensibility in mind to support future role-based access controls for restricted content (e.g., manager-only, department-specific information), but this capability is intentionally deferred to keep Phase 1 scope manageable.

AI Summary Feature: The AI-generated summaries with source citations are a core differentiator that reduces information overload by synthesizing knowledge from multiple documents. Implementation teams should evaluate LLM options for accuracy, citation quality, and cost-effectiveness during technical planning.

The system's value compounds over time as it learns from user behavior. Initial relevance may be lower until sufficient interaction data accumulates. Plan for iterative improvement cycles post-launch.

Consider phased rollout by department or use case to gather feedback and refine before full organizational deployment.