tractatus/public/locales/en/implementer.json

{
  "page": {
    "title": "Framework Implementation Guide | Tractatus",
    "description": "Technical documentation for integrating Tractatus framework: architecture, API reference, and code examples from production implementation."
  },
  "ui": {
    "breadcrumb_home": "Home",
    "breadcrumb_implementer": "Implementation Guide",
    "skip_to_content": "Skip to main content"
  },
  "hero": {
    "title": "External Governance Services for AI Systems",
    "subtitle": "Six architectural services addressing pattern override and decision traceability in agentic systems. Development framework for instruction persistence, boundary enforcement, and audit logging.",
    "value_prop_1_icon": "🏗️",
    "value_prop_1_title": "Architectural Separation",
    "value_prop_1_desc": "Governance runs external to AI model",
    "value_prop_2_icon": "💾",
    "value_prop_2_title": "Instruction Persistence",
    "value_prop_2_desc": "Validates instructions across context",
    "value_prop_3_icon": "📋",
    "value_prop_3_title": "Audit Trail by Design",
    "value_prop_3_desc": "MongoDB logs with service attribution"
  },
  "nav": {
    "quick_links_label": "Page sections",
    "how_it_works": "How It Works",
    "architecture": "Architecture",
    "hooks": "Hook Architecture",
    "deployment": "Deployment",
    "services": "Services",
    "api": "API Reference",
    "patterns": "Integration Patterns",
    "village_ai_arch": "Village AI",
    "steering_vectors_impl": "Steering Vectors",
    "taonga_registry": "Taonga Registry",
    "roadmap": "Roadmap"
  },
  "how_it_works": {
    "heading": "How It Works",
    "problem_title": "Pattern Override Challenge",
    "problem_desc": "AI systems operating across extended interactions may not maintain instruction consistency as context evolves. Instructions given early can be deprioritized or reinterpreted.",
    "approach_title": "External Architecture Approach",
    "approach_desc": "Tractatus services run external to the AI model, providing boundary validation, instruction classification, and audit logging through architectural separation.",
    "diagram_title": "Request Flow with Governance",
    "diagram_desc": "Example: AI decision flow with boundary enforcement—from user request through governance validation to human approval.",
    "diagram_alt": "Request Flow Sequence: How AI decisions are governed",
    "download_svg": "Download SVG"
  },
  "architecture": {
    "heading": "System Architecture",
    "six_services_title": "Six Core Services",
    "service_1": "BoundaryEnforcer (Tractatus 12.1-12.7)",
    "service_2": "InstructionPersistenceClassifier",
    "service_3": "CrossReferenceValidator",
    "service_4": "ContextPressureMonitor",
    "service_5": "MetacognitiveVerifier",
    "service_6": "PluralisticDeliberationOrchestrator",
    "main_flow_title": "Service Interaction Flow",
    "main_flow_alt": "Tractatus Framework Architecture: Shows how 6 governance services interact in sequence",
    "trigger_tree_title": "Service Trigger Conditions",
    "trigger_tree_alt": "Service Trigger Decision Tree: When each framework service activates",
    "system_arch_title": "System Architecture",
    "system_arch_desc": "High-level overview showing how the 6 governance services integrate with your application and data layer.",
    "system_arch_alt": "Tractatus System Architecture: Component interaction and data flow",
    "download_svg": "Download SVG",
    "download_svg_aria": "Download architecture main flow diagram as SVG",
    "download_trigger_svg_aria": "Download service trigger decision tree as SVG",
    "download_system_svg_aria": "Download system architecture diagram as SVG"
  },
  "hooks": {
    "badge": "ARCHITECTURAL ENFORCEMENT",
    "heading": "Hook Architecture: The Credibility Layer",
    "subtitle": "Tractatus governance is not voluntary compliance. PreToolUse hooks enforce boundaries before AI actions execute, making circumvention architecturally impossible.",
    "problem_title": "The Voluntary Compliance Problem",
    "problem_desc_1": "Traditional AI safety relies on the AI system \"choosing\" to follow rules embedded in training data or system prompts. These approaches assume the AI will maintain alignment regardless of context pressure or capability.",
    "problem_desc_2": "Tractatus addresses this through architectural enforcement: governance runs in a separate process that the AI cannot access, modify, or bypass.",
    "flow_title": "PreToolUse Hook Execution Flow",
    "flow_desc": "Before any file edit, write, or bash command executes, the hook intercepts the action and runs validation checks. Only if ALL checks pass (exit code 0) does the AI action proceed.",
    "flow_alt": "Hook Architecture: PreToolUse intercept flow showing validation and hard blocks",
    "examples_title": "Real Enforcement Examples",
    "example_1_icon": "🚫",
    "example_1_title": "GitHub URL Protection (inst_084)",
    "example_1_desc": "AI attempted to change GitHub repository URL from tractatus-framework (public) to tractatus (private).",
    "example_1_blocked": "BLOCKED:",
    "example_1_reason": "Repository URL modification detected",
    "example_1_detail": "Reason: Would expose private repo structure",
    "example_1_hook": "Hook: validate-file-edit.js line 448-505",
    "example_2_icon": "🚫",
    "example_2_title": "CSP Violation (inst_008)",
    "example_2_desc": "AI attempted to add inline style attribute to HTML element.",
    "example_2_blocked": "BLOCKED:",
    "example_2_reason": "Inline style detected",
    "example_2_pattern": "Pattern:",
    "example_2_hook": "Hook: validate-file-write.js line 68-149",
    "example_3_icon": "🚫",
    "example_3_title": "Governance File Protection (inst_027)",
    "example_3_desc": "AI attempted direct edit of instruction-history.json.",
    "example_3_blocked": "BLOCKED:",
    "example_3_reason": "inst_027 hard block",
    "example_3_use": "Use: scripts/add-instruction.js instead",
    "example_3_hook": "Hook: framework-audit-hook.js line 204-209",
    "example_4_icon": "⚠",
    "example_4_title": "Human Approval Required (BoundaryEnforcer)",
    "example_4_desc": "AI action touches values-sensitive file (privacy policy).",
    "example_4_status": "ASK HUMAN:",
    "example_4_reason": "Values decision detected",
    "example_4_requires": "Requires: Explicit human approval",
    "example_4_hook": "Hook: validate-file-write.js line 239-262",
    "tech_impl_title": "Technical Implementation",
    "process_sep_icon": "🔒",
    "process_sep_title": "Process Separation",
    "process_sep_desc": "Hooks execute in a separate Node.js process (child_process.spawn) with their own memory space. The AI agent cannot access, modify, or terminate the validation process.",
    "exit_code_icon": "⚙️",
    "exit_code_title": "Exit Code Enforcement",
    "exit_code_desc": "Hook validators return exit codes that Claude Code's architecture enforces:",
    "exit_code_0": "0 = PASS → Tool executes",
    "exit_code_2": "2 = HARD BLOCK → Tool execution halted, error shown to user",
    "audit_icon": "📊",
    "audit_title": "Audit Trail",
    "audit_desc": "Every hook execution is logged to .claude/metrics/hooks-metrics.json with timestamp, file, result, and reason. Blocks are separately tracked for analysis.",
    "why_matters_title": "Why Architectural Enforcement Matters",
    "why_matters_desc": "This is the fundamental difference between Tractatus and traditional AI safety approaches:",
    "voluntary_title": "❌ Voluntary Compliance",
    "voluntary_1": "AI \"chooses\" to follow rules",
    "voluntary_2": "Can drift under context pressure",
    "voluntary_3": "Pattern recognition may override instructions",
    "voluntary_4": "No verification before execution",
    "architectural_title": "✓ Architectural Enforcement",
    "architectural_1": "Hook intercepts before execution",
    "architectural_2": "Separate process, cannot be accessed",
    "architectural_3": "Exit codes enforced by runtime",
    "architectural_4": "Audit trail of all decisions"
  },
  "deployment": {
    "heading": "Deployment Architecture",
    "tech_stack_title": "Technology Stack",
    "runtime": "Runtime:",
    "runtime_value": "Node.js v20+",
    "database": "Database:",
    "database_value": "MongoDB 7.0+",
    "framework": "Framework:",
    "framework_value": "Express.js",
    "process": "Process:",
    "process_value": "Systemd (recommended)",
    "guide_title": "Production Deployment",
    "guide_desc": "Comprehensive deployment guide covering local development, production server configuration, Docker containerization, cloud deployment patterns (AWS, GCP, Kubernetes), database management, monitoring, and security best practices.",
    "covered_title": "📦 Covered in Guide:",
    "covered_1": "Local development setup",
    "covered_2": "Production server configuration",
    "covered_3": "Docker and Docker Compose",
    "covered_4": "Cloud deployment (AWS/GCP)",
    "also_title": "🔧 Also Includes:",
    "also_1": "Kubernetes manifests",
    "also_2": "Database backup and migration",
    "also_3": "SSL/TLS configuration",
    "also_4": "Monitoring and logging",
    "download_guide": "Download Deployment Guide (PDF)"
  },
  "services": {
    "heading": "Core Services",
    "service_1_name": "BoundaryEnforcer",
    "service_1_desc": "Implements Tractatus 12.1-12.7: AI cannot make values decisions without human approval.",
    "service_1_boundaries": "Boundaries:",
    "service_1_boundary_1": "VALUES (12.1) - Ethics, privacy, principles",
    "service_1_boundary_2": "INNOVATION (12.2) - Architectural decisions",
    "service_1_boundary_3": "WISDOM (12.3) - Strategic judgment",
    "service_1_boundary_4": "PURPOSE (12.4) - Goal definition",
    "service_2_name": "InstructionPersistenceClassifier",
    "service_2_desc": "Classifies instructions by quadrant (STRATEGIC/OPERATIONAL/TACTICAL/SYSTEM) and persistence level (HIGH/MEDIUM/LOW).",
    "service_2_classification": "Classification:",
    "service_2_class_1": "Quadrant (STRATEGIC/OPERATIONAL/TACTICAL/SYSTEM)",
    "service_2_class_2": "Persistence (HIGH/MEDIUM/LOW)",
    "service_2_class_3": "Temporal scope (PROJECT/SESSION/TASK)",
    "service_2_class_4": "Explicitness score (0.0-1.0)",
    "service_3_name": "CrossReferenceValidator",
    "service_3_desc": "Validates AI actions against stored instructions to prevent pattern recognition overrides.",
    "service_3_validation": "Validation:",
    "service_3_val_1": "Checks action against HIGH persistence instructions",
    "service_3_val_2": "Detects conflicts (pattern vs explicit instruction)",
    "service_3_val_3": "Provides correct parameters when rejected",
    "service_4_name": "ContextPressureMonitor",
    "service_4_desc": "Monitors token usage and context pressure, triggering safety protocols at thresholds.",
    "service_4_levels": "Pressure Levels:",
    "service_4_level_1": "NORMAL (0-50%) - Full operation",
    "service_4_level_2": "ELEVATED (50-75%) - Increase verification",
    "service_4_level_3": "HIGH (75-90%) - Reduce complexity",
    "service_4_level_4": "CRITICAL (90%+) - Suggest handoff",
    "service_5_name": "MetacognitiveVerifier",
    "service_5_desc": "Verifies action reasoning and confidence, requiring confirmation for low-confidence actions.",
    "service_5_verification": "Verification:",
    "service_5_ver_1": "Confidence scoring (0.0-1.0)",
    "service_5_ver_2": "Selective mode (HIGH persistence only)",
    "service_5_ver_3": "Requires confirmation if confidence < 0.7",
    "service_6_name": "PluralisticDeliberationOrchestrator",
    "service_6_desc": "Manages multi-stakeholder deliberation ensuring value pluralism in decisions.",
    "service_6_features": "Features:",
    "service_6_feature_1": "Stakeholder perspective tracking",
    "service_6_feature_2": "Value conflict detection",
    "service_6_feature_3": "Deliberation session management",
    "service_6_feature_4": "Precedent storage",
    "source_code_label": "📁 Source Code",
    "source_code_desc": "Code patterns and examples are available in the GitHub repository."
  },
  "api": {
    "heading": "API Reference",
    "boundary_method": "BoundaryEnforcer.checkBoundary()",
    "boundary_keywords": "Keywords detected:",
    "boundary_keywords_list": "value, principle, ethic, moral, should, ought, right, wrong, privacy, policy, trade-off, etc.",
    "classifier_method": "InstructionPersistenceClassifier.classify()",
    "classifier_quadrants": "Quadrants:",
    "classifier_quadrants_list": "STRATEGIC, OPERATIONAL, TACTICAL, SYSTEM, STORAGE",
    "classifier_persistence": "Persistence:",
    "classifier_persistence_desc": "HIGH (override all), MEDIUM (session-scoped), LOW (can be superseded)",
    "validator_method": "CrossReferenceValidator.validate()",
    "monitor_method": "ContextPressureMonitor.analyzePressure()"
  },
  "examples": {
    "heading": "Integration Examples",
    "express_title": "Express Middleware Integration",
    "validation_title": "Instruction Validation",
    "mongodb_title": "MongoDB Data Models",
    "governance_rule": "GovernanceRule",
    "audit_log": "AuditLog"
  },
  "patterns": {
    "heading": "Integration Patterns",
    "intro": "Common architectural patterns for integrating Tractatus into existing systems.",
    "middleware_title": "Middleware Integration",
    "middleware_desc": "Insert governance checks as middleware in your request pipeline. Suitable for API-based AI systems.",
    "middleware_usecase": "Use Case:",
    "middleware_usecase_value": "REST APIs, Express.js applications",
    "event_title": "Event-Driven Governance",
    "event_desc": "Trigger governance checks via events. Suitable for async workflows and microservices.",
    "event_usecase": "Use Case:",
    "event_usecase_value": "Message queues, event buses, async processing",
    "hook_title": "Pre/Post-Action Hooks",
    "hook_desc": "Validate actions before and after execution. Current production pattern for Claude Code.",
    "hook_usecase": "Use Case:",
    "hook_usecase_value": "LLM tool use, autonomous agents",
    "sidecar_title": "Sidecar Governance Service",
    "sidecar_desc": "Deploy governance as a separate service. Suitable for multi-LLM or polyglot environments.",
    "sidecar_usecase": "Use Case:",
    "sidecar_usecase_value": "Kubernetes, containerized deployments"
  },
  "village_ai_arch": {
    "heading": "Village AI: Two-Model Sovereign Architecture",
    "intro": "Production deployment of Tractatus governance on locally-trained open-source models, demonstrating framework portability beyond Claude.",
    "arch_title": "Two-Model Routing Architecture",
    "arch_intro": "Village AI uses a dual-model design where queries are routed based on complexity and governance requirements. Both models run locally with full Tractatus governance in the inference pipeline.",
    "fast_title": "Fast Model: Llama 3.2 3B",
    "fast_1": "<strong>Purpose:</strong> Common queries with pre-filtered governance",
    "fast_2": "<strong>Fine-tuning:</strong> QLoRA on domain-specific data",
    "fast_3": "<strong>Governance:</strong> Lightweight boundary check before response",
    "deep_title": "Deep Model: Llama 3.1 8B",
    "deep_1": "<strong>Purpose:</strong> Complex reasoning with full governance pipeline",
    "deep_2": "<strong>Fine-tuning:</strong> QLoRA with extended context governance",
    "deep_3": "<strong>Governance:</strong> Full 6-service pipeline (BoundaryEnforcer through PDO)",
    "routing_title": "Model Routing Logic",
    "impl_title": "Implementation Details",
    "stat_sovereignty": "100% Local",
    "stat_sovereignty_desc": "Training data never leaves infrastructure",
    "stat_governance": "6 Services",
    "stat_governance_desc": "Full Tractatus governance in inference pipeline",
    "stat_first": "First Non-Claude",
    "stat_first_desc": "Validates Tractatus portability beyond Anthropic",
    "status_note": "<strong>Status:</strong> Inference governance operational. Sovereign training pipeline installation in progress. Production deployment at Village Home Trust validates governance portability across model architectures.",
    "cta": "Village AI Architecture Details →"
  },
  "steering_impl": {
    "heading": "Steering Vectors: Inference-Time Bias Correction",
    "intro": "Techniques for correcting model behaviour at inference time without retraining, applicable to QLoRA-fine-tuned models like those in Village AI.",
    "paper_ref": "Reference:",
    "paper_title": "Steering Vectors and Mechanical Bias in Sovereign AI Systems (STO-RES-0009 v1.1, February 2026)",
    "techniques_title": "Key Techniques for Implementers",
    "caa_title": "Contrastive Activation Addition (CAA)",
    "caa_desc": "Extract activation differences between contrasting prompts, then add scaled vectors during inference to steer model behaviour toward desired attributes.",
    "repe_title": "Representation Engineering (RepE)",
    "repe_desc": "Identify and manipulate internal representations of concepts (honesty, safety, fairness) through probing and directional manipulation of model activations.",
    "fairsteer_title": "FairSteer",
    "fairsteer_desc": "Identify bias-critical layers through probing classifiers, then apply targeted corrections at those specific layers rather than globally.",
    "dso_title": "Direct Steering Optimisation (DSO)",
    "dso_desc": "Optimise steering vectors using preference datasets (DPO-style), enabling training-free bias correction that learns from human preference data.",
    "key_distinction": "<strong>Key Distinction:</strong> The paper distinguishes between mechanical bias (pre-reasoning, embedded in model weights — addressable by steering vectors) and reasoning bias (deliberative, arising during inference — requiring governance framework approaches like Tractatus).",
    "research_link": "Full Research Details →",
    "paper_link": "Read Paper (HTML)"
  },
  "taonga_registry": {
    "heading": "Taonga Registry: Governed Data Objects for Indigenous Sovereignty",
    "intro": "Architectural patterns for treating steering packs and model configurations as governed data objects with provenance tracking and community-controlled access.",
    "status_title": "Conceptual Architecture — In Peer Review",
    "status_text": "Based on STO-RES-0010 v0.1 DRAFT, currently in indigenous peer review. Written without Maori co-authorship — presented as a starting point for collaboration. Not ready for production implementation.",
    "concepts_title": "Key Architectural Concepts",
    "packs_title": "Steering Packs as Governed Objects",
    "packs_desc": "Model configurations, fine-tuning data, and steering vectors are treated as taonga (treasures) with full provenance tracking, access control, and community consent requirements.",
    "provenance_title": "Provenance & Withdrawal Rights",
    "provenance_desc": "Every data object tracks its full lineage. Communities retain the right to withdraw their data and configurations, with architectural enforcement of removal propagation.",
    "polycentric_title": "Polycentric Governance Integration",
    "polycentric_desc": "Extends PluralisticDeliberationOrchestrator with iwi/community as co-equal governance authorities. Access decisions require multi-party consensus.",
    "care_title": "CARE Principles Implementation",
    "care_desc": "Architecturally enforces Collective benefit, Authority to control, Responsibility, and Ethics for indigenous data governance within the Tractatus framework.",
    "research_link": "Research Context →",
    "paper_link": "Read Draft Paper (HTML)"
  },
  "roadmap": {
    "heading": "Development Roadmap & Collaboration",
    "intro": "Tractatus is an active research framework. We welcome collaboration on priority development areas.",
    "priority_title": "🚀 Priority Areas for Development",
    "priority_desc": "These initiatives represent high-impact opportunities for framework enhancement. Technical contributors, researchers, and organizations are encouraged to engage.",
    "multi_llm_icon": "🤖",
    "multi_llm_title": "Multi-LLM Support",
    "multi_llm_badge": "First Deployment Live",
    "multi_llm_status": "Status: First Non-Claude Deployment Operational",
    "multi_llm_desc": "Village AI deploys Tractatus governance on Llama 3.1 8B and Llama 3.2 3B via QLoRA fine-tuning — the first validated non-Claude deployment. Extends governance portability to open-source models with full 6-service pipeline.",
    "multi_llm_challenges": "Next Steps:",
    "multi_llm_challenges_desc": "GPT-4 and Gemini adapters, provider-specific tool/function calling, sovereign training pipeline completion",
    "bindings_icon": "📚",
    "bindings_title": "Language Bindings",
    "bindings_status": "Status: Community Interest",
    "bindings_desc": "Python, Go, and Rust implementations to serve broader developer communities. Core logic is portable; MongoDB integration is universal.",
    "bindings_value": "Value:",
    "bindings_value_desc": "Enable polyglot AI stacks, performance-critical applications (Rust), data science workflows (Python)",
    "cloud_icon": "☁️",
    "cloud_title": "Cloud-Native Deployment",
    "cloud_status": "Status: Reference Architectures Needed",
    "cloud_desc": "Terraform/Helm charts for AWS, Azure, GCP. Include managed MongoDB (Atlas), auto-scaling, and monitoring integration.",
    "cloud_deliverables": "Deliverables:",
    "cloud_deliverables_desc": "Reference IaC templates, cost optimization guides, security hardening checklist",
    "integrations_icon": "🔗",
    "integrations_title": "AI Framework Integration",
    "integrations_status": "Status: Conceptual",
    "integrations_desc": "Adapters for LangChain, Semantic Kernel, AutoGPT, and CrewAI. Enable governance for existing agent frameworks.",
    "integrations_approach": "Approach:",
    "integrations_approach_desc": "Plugin/middleware architecture that wraps agent actions with governance checks",
    "performance_icon": "⚡",
    "performance_title": "Enterprise-Scale Performance",
    "performance_status": "Status: Validation Needed",
    "performance_desc": "Optimize for 1000+ concurrent AI agents. Requires caching strategies, rule compilation, and distributed audit logging.",
    "performance_target": "Metrics Target:",
    "performance_target_desc": "< 5ms governance overhead per decision, 99.9% uptime, horizontal scalability",
    "extended_icon": "🛡️",
    "extended_title": "Extended Governance Services",
    "extended_status": "Status: Research",
    "extended_desc": "Cost monitoring, rate limiting, PII detection, adversarial prompt defense. Domain-specific services for regulated industries.",
    "extended_examples": "Examples:",
    "extended_examples_desc": "FinancialComplianceService, HealthcarePrivacyService, CostBudgetEnforcer",
    "training_title": "Sovereign Training Pipeline",
    "training_status": "Status: In Progress",
    "training_desc": "Governance-inside-the-training-loop for community-controlled models. Extends Tractatus from inference-time governance to training-time governance, ensuring data sovereignty from fine-tuning through deployment.",
    "training_deliverables": "Deliverables:",
    "training_deliverables_desc": "QLoRA training with governance constraints, data provenance tracking, community consent pipeline",
    "taonga_title": "Taonga Data Registry",
    "taonga_status": "Status: Conceptual / In Peer Review",
    "taonga_desc": "Governed data objects with provenance tracking, withdrawal rights, and polycentric community governance. Extends PluralisticDeliberationOrchestrator for indigenous data sovereignty.",
    "taonga_reference": "Reference:",
    "taonga_reference_desc": "STO-RES-0010 v0.1 DRAFT — in indigenous peer review",
    "involved_title": "Get Involved",
    "involved_desc": "Tractatus is Apache 2.0 licensed research. We welcome contributions, pilot implementations, and collaborative research partnerships.",
    "contributors_title": "👨‍💻 Technical Contributors",
    "contributors_desc": "Implement features, fix bugs, improve documentation",
    "contributors_link": "→ Contributing Guide",
    "research_title": "🔬 Research Partners",
    "research_desc": "Validation studies, academic collaboration, case studies",
    "research_email": "→ research@agenticgovernance.digital",
    "pilots_title": "🏢 Organization Pilots",
    "pilots_desc": "Production deployments, enterprise requirements, feedback loops",
    "pilots_link": "→ Submit Case Study",
    "why_collab": "Why Collaborate?",
    "why_collab_desc": "Tractatus addresses real gaps in AI safety. Early adopters shape the framework's evolution and gain expertise in structural AI governance—a differentiating capability as regulatory requirements mature."
  },
  "resources": {
    "heading": "Resources",
    "docs_title": "Documentation",
    "docs_github": "→ GitHub Repository",
    "docs_full": "→ Full Documentation",
    "docs_demos": "→ Live Demos",
    "docs_glossary": "→ Glossary of Terms",
    "docs_glossary_de": "→ Glossar der Begriffe (DE)",
    "docs_glossary_fr": "→ Glossaire des Termes (FR)",
    "reference_title": "Reference Implementation",
    "reference_desc": "This website (agenticgovernance.digital) runs on Tractatus governance.",
    "reference_services": "Services:",
    "reference_services_value": "22 governance components",
    "reference_models": "Data Models:",
    "reference_models_value": "29 MongoDB schemas",
    "reference_tests": "Test Coverage:",
    "reference_tests_value": "35 test suites",
    "reference_repo": "→ Collaboration Repository",
    "support_title": "Support",
    "support_desc": "Questions about implementation or integration?",
    "support_issues": "→ GitHub Issues",
    "support_discussions": "→ GitHub Discussions",
    "support_email": "→ research@agenticgovernance.digital",
    "support_faq": "→ Frequently Asked Questions"
  },
  "share_cta": {
    "heading": "Help us reach the right people.",
    "description": "If you know researchers, implementers, or leaders who need structural AI governance solutions, share this with them.",
    "copy_link": "Copy Link",
    "email": "Email",
    "linkedin": "LinkedIn"
  }
}