The challenge & our answer
The Problem
Social-profit organizations face two knowledge gaps: they can't find platform help when they need it, and valuable field insights — best practices, case studies, implementation approaches from sustainable development work worldwide — remain locked in scattered reports and institutional silos.
The Solution
Action Network Worldwide provides a unified knowledge base where platform FAQs, guides, and articles live alongside community-contributed field knowledge. Content is searchable, tagged for discovery, and available at two visibility levels: public content for anyone, and network-scoped entries for authenticated members only. AI assists with content standardization — classifying submissions, extracting metadata, and suggesting tags — while humans maintain editorial control.
Key Capabilities
Six content types for every knowledge need
FAQs for quick answers, articles for platform updates and sector overviews, step-by-step guides for both platform usage and field implementation, case studies documenting real-world outcomes, best practices capturing proven methodologies, and curated resource collections.
Public and network-scoped visibility
Every knowledge entry has a scope: public content is visible to anyone (no login required), while network-scoped entries are available only to authenticated members. Organizations can share detailed field reports with the community while keeping sensitive information appropriately restricted.
AI-assisted content standardization
When users submit content — whether typed, pasted, or uploaded as PDF/DOCX/TXT/MD files — AI classifies the content type, suggests a title and summary, assigns a category and tags from the platform taxonomy, and returns per-field confidence scores. Users review suggestions and adjust before saving.
Full-text search across all content
Search works across titles, summaries, and full content. Filter by content type, category, or tags. Public search shows only public-scoped entries; authenticated search includes network-scoped content. Results are ranked by relevance.
Cross-referencing for discovery
Every knowledge entry can link to related entries via explicit cross-references and shared tags. The system suggests related content at the bottom of each entry — helping users discover connected knowledge without manual searching.
Initial content generated by Claude Code
Platform FAQs, guides, and articles are generated offline using Claude Code CLI — which reads the actual codebase, API routes, and documentation for full context. Generated content is reviewed by developers, imported via the admin panel, and refined by admins as needed.
How It Works
Submit or upload field knowledge
Write directly in the platform, paste from existing documents, or upload PDF/DOCX/TXT/MD files. The AI extraction pipeline reads the content and prepares it for classification.
AI classifies and suggests metadata
The system analyzes the content and returns structured suggestions: content type, title, summary, category, tags, and a scope recommendation. Each field includes a confidence score so you know which suggestions are reliable.
Review, adjust, and save
You see the AI suggestions with confidence badges. Adjust any field that needs refinement. The AI suggestions are advisory only — you have full editorial control.
Content enters review workflow
Submitted entries go to the admin review queue. Admins approve, edit, or request changes. Once published, the entry becomes discoverable via search, category browsing, and tag filtering.
Discovery through search and cross-references
Published knowledge is searchable by full text, filterable by type and category, and cross-referenced via tags and related-entry links. Public entries are visible to anyone; network-scoped entries require authentication.