Content Sync to AI Search (Foucault)

1 Overview

The hooks content sync system automatically dumps documentation and site content into Foucault, our internal AI-powered search and knowledge management system. This provides intelligent search, content discovery, and contextual assistance across all ECIC documentation and operational knowledge.

System Location: ~/dev/workbench/hooks/packages/hook-content-sync/ Target System: Foucault AI Search Engine Sync Frequency: 4-hour cycles with real-time updates for critical content Content Sources: Internal docs, public site, process documentation, compliance materials

2 Architecture

graph TB
    A[Documentation Sites] --> D[Content Sync Engine]
    B[Process Documentation] --> D
    C[Compliance Materials] --> D
    D --> E[Content Processing Pipeline]
    E --> F[Semantic Analysis]
    F --> G[Vector Embeddings]
    G --> H[Foucault AI Search]
    H --> I[Query Interface]
    H --> J[Context API]
    H --> K[Knowledge Graphs]

    style D fill:#e3f2fd
    style H fill:#f3e5f5
    style I fill:#e8f5e8

3 Content Ingestion Pipeline

3.1 Source Systems

Ethical Capital Docs (/Users/srvo/ethicalcapital-docs/) - Internal documentation, compliance, processes
Public Site Content (/Users/srvo/ethicic-public/) - Published articles, strategy guides, educational content
Workbench Documentation (/Users/srvo/dev/workbench/) - Technical documentation, API references
Hooks Data Catalog - Operational data, client records, compliance matrices

3.2 Content Processing Workflow

3.2.1 1. Content Discovery & Extraction

graph LR
    A[File System Scanner] --> B[Content Extraction]
    B --> C[Metadata Enrichment]
    C --> D[Content Classification]
    D --> E[Quality Validation]

Operations: - File System Scanning: Recursive directory traversal with pattern matching - Format Support: Markdown (.qmd, .md), structured data (CSV, JSON, YAML) - Metadata Extraction: Frontmatter parsing, file attributes, modification timestamps - Content Classification: Document type detection, compliance category mapping - Quality Filtering: Minimum content thresholds, duplicate detection

3.2.2 2. Semantic Processing

graph LR
    A[Raw Content] --> B[Text Processing]
    B --> C[Entity Recognition]
    C --> D[Topic Modeling]
    D --> E[Relationship Mapping]

Operations: - Text Normalization: Markdown conversion, link resolution, code block handling - Entity Extraction: Company names, regulations, process names, vendor systems - Topic Classification: Compliance, operations, client management, technical documentation - Relationship Discovery: Cross-document references, dependency mapping, workflow connections

3.2.3 3. Vector Embedding & Indexing

graph LR
    A[Processed Content] --> B[Embedding Generation]
    B --> C[Vector Storage]
    C --> D[Index Construction]
    D --> E[Search Optimization]

Operations: - Vector Embeddings: High-dimensional semantic representations for content chunks - Hierarchical Indexing: Document-level, section-level, and paragraph-level granularity - Contextual Clustering: Related content grouping for improved retrieval - Search Optimization: Query performance tuning, relevance scoring calibration

3.3 Data Flow Schedule

3.3.1 Real-Time Sync (High Priority)

Compliance Updates: SEC filings, regulatory changes, client agreement modifications
Critical Processes: Emergency procedures, security incidents, operational alerts
Client Communications: Important policy changes, service updates

3.3.2 4-Hour Sync Cycles (Standard)

Process Documentation: SOP updates, workflow modifications, vendor integrations
Technical Documentation: API changes, system architecture updates, deployment guides
Knowledge Base: Educational content, best practices, troubleshooting guides

3.3.3 Daily Batch Processing (Comprehensive)

Full Content Reindexing: Complete document corpus refresh
Relationship Mapping: Cross-reference updates and dependency analysis
Quality Assurance: Content validation, broken link detection, metadata verification

4 Foucault AI Search Features

4.1 Intelligent Query Processing

Natural Language Queries: “What are the requirements for Utah RIA compliance?”
Context-Aware Results: Results prioritized by user role and current work context
Multi-Modal Search: Text, document structure, and metadata-based retrieval
Query Expansion: Automatic inclusion of related terms and concepts

4.2 Content Discovery

Semantic Browsing: Explore related documents and concepts automatically
Topic Clustering: Browse content by subject area, regulation, or process type
Relationship Visualization: Interactive maps of document relationships and dependencies
Trend Analysis: Content creation patterns, update frequencies, knowledge gaps

4.3 Contextual Intelligence

Role-Based Results: Different search results based on compliance, ops, or technical roles
Workflow Integration: Search results include relevant next steps and action items
Compliance Context: Automatic regulatory framework mapping for policy questions
Process Guidance: Step-by-step procedures surfaced contextually during searches

5 Technical Implementation

5.1 Content Sync Engine

// Core sync functionality
interface ContentSyncConfig {
  sources: ContentSource[];
  processors: ContentProcessor[];
  destinations: SearchIndex[];
  schedule: SyncSchedule;
}

interface ContentSource {
  path: string;
  patterns: string[];
  metadata_extractors: MetadataExtractor[];
  processors: string[];
}

5.2 Processing Pipeline

Parallel Processing: Multiple content sources processed simultaneously
Incremental Updates: Only changed content reprocessed to optimize performance
Error Handling: Robust retry logic with exponential backoff for failed operations
Monitoring: Comprehensive logging and alerting for sync failures

5.3 Search Index Structure

{
  "document_id": "compliance/utah-ria-compliance-framework",
  "title": "Utah RIA Compliance Framework",
  "content_type": "compliance_documentation",
  "sections": [
    {
      "heading": "AUM Thresholds",
      "content": "Utah Rule R164-5-1...",
      "embedding": [0.123, -0.456, ...],
      "entities": ["SEC Rule 204-2", "Utah RIA", "$100M threshold"],
      "relationships": ["compliance/records-management-matrix"]
    }
  ],
  "metadata": {
    "owner": "Ops: Compliance",
    "last_reviewed": "2025-09-25",
    "review_cycle": "120d",
    "compliance_categories": ["SEC Rule 204-2", "State Registration"]
  }
}

6 Integration Points

6.1 Documentation Systems

Quarto Integration: Automatic processing of .qmd files with metadata preservation
Git Hook Triggers: Content sync triggered by documentation commits
Link Resolution: Internal cross-references maintained and searchable
Version Tracking: Document history and change attribution

6.2 Compliance Systems

SEC Records Matrix: Automated mapping to Rule 204-2 categories
Retention Policies: Search results include relevant retention requirements
Audit Trail Integration: Search queries logged for compliance review
Regulatory Updates: Automatic flagging of impacted documents

6.3 Operational Systems

Process Documentation: Workflow steps and dependencies searchable
Vendor Integration: System documentation accessible through search
Client Context: Search results filtered by client relevance and permissions
SLA Tracking: Process documentation includes performance requirements

7 Search Quality & Performance

7.1 Content Quality Metrics

Coverage Completeness: Percentage of documentation indexed and searchable
Freshness Score: Average age of content across different document types
Cross-Reference Integrity: Broken links and missing dependencies detected
Metadata Completeness: Frontmatter and classification coverage

7.2 Search Performance

Query Response Time: Sub-second response for standard searches
Relevance Scoring: User feedback integration for result quality improvement
Query Success Rate: Percentage of searches returning useful results
User Satisfaction: Search result usage and refinement patterns

7.3 System Health Monitoring

Sync Success Rates: Content ingestion completion percentages
Processing Latency: Time from content update to search availability
Index Size Growth: Storage utilization and performance impact
Error Pattern Analysis: Common failure modes and resolution tracking

8 Security & Access Control

8.1 Content Security

Permission Mapping: Document access controls preserved in search results
PII Handling: Sensitive information masked or excluded from indexing
Audit Logging: All search queries and content access logged
Data Classification: Content sensitivity levels integrated into search filtering

8.2 Search Security

Role-Based Access: Search results filtered by user permissions
Query Logging: Comprehensive audit trail for compliance review
Data Isolation: Client-specific content appropriately segregated
Encryption: Search indices and query traffic encrypted

9 Operational Procedures

9.1 Daily Operations

Sync Health Check: Automated validation of content sync pipeline
Index Verification: Search result quality and coverage assessment
Performance Monitoring: Query response times and system resource utilization
Error Review: Failed sync operations and resolution tracking

9.2 Weekly Maintenance

Content Quality Review: Manual validation of automated classifications
Search Analytics: Query patterns and user behavior analysis
Index Optimization: Performance tuning and storage management
Relationship Validation: Cross-document reference accuracy verification

9.3 Monthly Assessment

Coverage Analysis: Documentation gaps and indexing completeness
User Feedback Integration: Search result improvements based on usage patterns
Compliance Review: Audit trail analysis and regulatory requirement adherence
System Capacity Planning: Growth projections and resource allocation

10 Troubleshooting & Maintenance

10.1 Common Issues

Content Sync Failures

# Check sync status
cd ~/dev/workbench/hooks/packages/hook-content-sync/
cat logs/sync-status.json | jq '.last_sync'

# Restart sync process
npm run sync:restart

Search Quality Problems

# Rebuild search index
npm run index:rebuild

# Validate content quality
npm run validate:content

Performance Issues

# Monitor resource usage
npm run monitor:performance

# Optimize search indices
npm run optimize:indices

10.2 Log Locations

Sync Logs: ~/dev/workbench/hooks/packages/hook-content-sync/logs/
Search Logs: /var/log/foucault-search/
Performance Metrics: ~/dev/workbench/hooks/data/metrics/content-sync/
Error Reports: ~/dev/workbench/hooks/data/errors/content-sync/

11 Future Enhancements

11.1 Planned Improvements

Real-Time Collaboration: Live document editing with immediate search updates
Advanced Analytics: Content usage patterns and knowledge discovery insights
AI-Assisted Content Creation: Automated documentation generation and maintenance
Multi-Language Support: Translation and localization capabilities for global expansion

11.2 Integration Roadmap

Claude Code Integration: Direct search access from development environment
Client Portal Integration: Filtered search results for client-accessible content
Mobile Access: Optimized search interface for mobile device usage
Voice Search: Audio query processing for hands-free operation

The Foucault AI search system transforms ECIC’s comprehensive documentation into an intelligent, searchable knowledge base that enhances operational efficiency, compliance management, and strategic decision-making across all business functions.