Settings & Quality Assurance
Configure production defaults, appearance, and accessibility — and review the responsible-AI, quality-assurance, and enterprise-implementation framework that governs every package the Factory produces. All settings are presentational for this demonstration; content is illustrative and requires human editorial review.
🎨 Appearance
Choose how the workspace looks. Your preference is saved locally in this browser.
A low-glare theme for long authoring and review sessions.
♿ Accessibility A floating accessibility toolbar (high-contrast mode, larger text, and reduced motion) is available site-wide via the toolbar control. Every screen uses semantic HTML, skip links, and full keyboard navigation.
🏭 Production defaults
Set the defaults the Factory and generators start from. Presentational only in this demonstration.
🚀 Future AI features
On the platform roadmap. These capabilities are planned, not yet enabled in this demonstration.
- Voice authoring Soon — draft and revise content hands-free by speaking to the Factory.
- AI avatars Soon — generate presenter-led video with synthetic narrators and lip-sync.
- Automatic translation Soon — one source package, many languages, with localization review.
- Real-time collaboration Soon — co-authoring and live editorial review across teams.
- API integrations Soon — push and pull content programmatically to LMS, SIS, and CMS systems.
- Enterprise AI agents Soon — autonomous production agents that build full catalogs to spec.
- Content quality scoring Soon — automated readability, alignment, and rigor diagnostics per asset.
- Curriculum comparison Soon — gap analysis against standards, frameworks, and competitor catalogs.
- Adaptive publishing Soon — content that re-renders by reading level, device, and learner profile.
- Generative simulations Soon — interactive, dependency-free simulations produced from a prompt.
Responsible AI & Quality Assurance
AI produces at scale; editors guarantee quality. Every asset the Factory generates is a draft that passes through this framework before it can be published.
👤 Human editorial review
No asset publishes without a qualified editor reviewing, correcting, and approving it. The AI proposes; people decide and own the result.
🎓 Pedagogical quality
Content is checked for instructional soundness, standards alignment, age-appropriate rigor, and coherent learning progressions.
⚖️ Bias monitoring
Materials are reviewed for cultural responsiveness, representation, and fairness so content serves every learner equitably.
♿ Accessibility review
Alt text, reading level, captions, transcripts, color contrast, and keyboard access are verified against WCAG before release.
©️ Copyright considerations
Source attribution, licensing, and originality are confirmed; AI output is treated as a draft to be vetted, never blindly shipped.
🔖 Version control
Every asset is versioned with a full revision history, so changes are traceable and prior states are recoverable.
✅ Content validation
Factual accuracy, answer keys, rubrics, and standards tags are validated by subject-matter experts before publishing.
🤖 Responsible AI use
Clear policies define where and how AI assists, with safeguards against over-reliance and against presenting drafts as finished work.
🔍 Transparency
AI-generated drafts are clearly labeled, with visible provenance so reviewers and downstream users know what was machine-assisted.
📈 Continuous improvement
Editorial findings feed back into prompts, templates, and review checklists so quality compounds over time.
👤 Editorial gate This framework is the non-negotiable gate between AI production and publishing. All AI is simulated client-side for demonstration; sample content is illustrative.
🏢 Implementation guide
Presented as if to an educational publisher, edtech company, school district, ministry of education, or online school adopting the Factory at scale.
- Implementation strategy — start with a focused pilot on one catalog or grade band against a baseline, prove time-to-production and quality, then expand title-by-title and team-by-team rather than all at once.
- Author training — coach authors and editors to write effective prompts, read AI output critically, and edit and approve with authority so the human stays firmly in command.
- AI governance — establish policy up front: where AI may assist, data handling and retention, provenance labeling, and accountability for published content.
- Quality assurance — operationalize the responsible-AI & QA framework above as required review gates, with checklists, sign-off roles, and audit trails.
- Editorial workflow — define draft → review → revise → approve stages with clear ownership, so every asset moves through human review before it advances.
- Publishing workflow — standardize export targets (web, PDF, PPT, Word, eBook, online course, SCORM/LMS) and branding so output is consistent across formats.
- Scaling strategy — template the house style, reuse approved components, and grow capacity by adding teams and brands without re-solving production each time.
- Success metrics — track production time saved, assets shipped per editor, defect and rework rates, accessibility-pass rates, and standards-alignment coverage.
- Enterprise deployment — add role-based access, SSO, approval hierarchies, brand management, audit logging, and integration with existing LMS/SIS/CMS systems.
🗄️ Data
This demonstration runs entirely in your browser. Nothing leaves this device; your saved content, history, and stats live only in this browser's localStorage.
Local keys used by this demonstration: cf:theme (appearance), cf:library (saved content), cf:hist (Factory chat history), and cf:stats (production counters). Clearing data above removes cf:library, cf:hist, and cf:stats; your cf:theme preference is kept.