How intle uses AI to generate learning content.

What may be sent to AI providers

• - The brief or prompt the creator writes.
• - Text extracted from uploaded PDF, DOCX, PPTX, TXT, and supported source files.
• - Uploaded source images (PNG, JPG, GIF, WebP), and selected rendered PDF figures or scanned pages, may be sent as image inputs to the configured vision model. Its text analysis can then become source material for planning and generation.
• - Text fetched from user-supplied web, YouTube, or Vimeo source URLs after SSRF-safe fetching.
• - Generated content, edit instructions, repair prompts, and model outputs needed to complete the request.
• - Generation metadata needed for routing, schema validation, retries, quality gates, and cost telemetry.

Uploaded documents are read by the application to extract relevant text and source structure for AI prompts; image analysis also sends the selected image or rendered page pixels. Uploaded files may contain personal data if a user includes it. Users should avoid uploading unnecessary personal, confidential, or special-category data.

What is not intentionally sent

• - Card numbers or payment-method data.
• - Stripe payment method details.
• - Raw billing identifiers unless a user explicitly includes them in a brief or uploaded file.
• - Account authentication secrets, magic-link tokens, session cookies, or participant HMAC secrets.
• - Admin-only notes or internal security logs unrelated to the generation request.

Human oversight

Generated output is editable. Creators are expected to review, correct, and approve content before publishing, hosting, or exporting a SCORM package, especially for compliance-sensitive, health and safety, legal, medical, or regulated training.

Quality and risk controls

• - Schema-bound generation through the AI SDK where structured content is required.
• - Content-quality validators for accessibility, visual density, cognitive load, duplication, and brief alignment.
• - Schema validation, deterministic structural repairs and selected quality checks; a rejected course may be regenerated as a fresh complete candidate from the approved plan and source evidence.
• - Recovery varies by task, failure and request budget. It can include a fresh schema-valid candidate, a compatible fallback provider, or simplified generation; these are not fixed steps on every request.
• - Creator review and editing before publishing, hosting, or SCORM export.

These checks can detect structural and selected quality problems; they do not verify every factual claim or replace creator review.