Elai

Converts written text or URL-sourced content into video presentations by parsing input, generating a visual storyboard layout, synthesizing a presenter avatar performance, and compositing all elements into a final video file. The system likely uses a content-to-scene mapping pipeline that identifies key narrative segments, assigns visual treatments, and synchronizes avatar lip-sync with generated or provided voiceover audio.

Generates natural-sounding voiceover audio in 75 languages by routing text through language-specific text-to-speech (TTS) engines, likely using a multi-provider abstraction layer (e.g., Google Cloud TTS, Azure Speech Services, or proprietary neural TTS models) that selects the optimal voice profile based on language, accent preference, and gender. The system handles phonetic normalization, prosody adjustment, and audio normalization to match video timing.

Analyzes input text to identify narrative segments, key topics, and visual transition points, then automatically generates a scene-by-scene storyboard with layout suggestions, background selections, and avatar positioning. This likely uses NLP-based text segmentation (e.g., sentence clustering, topic modeling) combined with a rule-based or learned mapping from semantic content to visual templates, enabling users to skip manual shot planning.

Provides a library of pre-trained AI avatars with configurable appearance (skin tone, clothing, hairstyle, gender presentation) and synthesizes their performance (gestures, facial expressions, head movements) synchronized to voiceover audio using neural animation models. The system likely uses a latent space representation of avatar characteristics and motion synthesis via diffusion or transformer-based models that generate frame-by-frame animations conditioned on audio prosody and script semantics.

Enables batch creation of videos with variable content (e.g., recipient name, company, custom details) by accepting a CSV or JSON template with placeholders, then generating multiple video variants in parallel. The system likely uses a templating engine that substitutes variables into scripts, regenerates voiceover and storyboards per variant, and manages a job queue for distributed video encoding, enabling campaigns with hundreds of personalized videos.

Accepts a URL (blog post, article, landing page) and automatically extracts text content, metadata, and visual assets, then generates a video by parsing the extracted content through the text-to-video pipeline. The system likely uses web scraping (e.g., Puppeteer, Cheerio) with content extraction heuristics (e.g., removing boilerplate, identifying main content blocks) and optional visual asset harvesting to populate video backgrounds.

Provides an interactive editor for refining generated videos by allowing users to edit scripts, adjust storyboard scenes, swap avatars, modify voiceover timing, add captions, and adjust visual effects. The editor likely uses a timeline-based UI (similar to Premiere or DaVinci Resolve) with real-time preview and a render queue that regenerates only changed segments rather than re-encoding the entire video, enabling rapid iteration.

Hosts generated videos on Elai's CDN and provides shareable links with built-in analytics tracking (view count, watch time, engagement metrics). The system likely uses a video delivery network (CDN) for low-latency streaming, embeds tracking pixels or JavaScript SDKs in video players, and aggregates analytics in a dashboard. This enables users to track video performance without external analytics tools.

Exposes REST or GraphQL APIs that enable developers to programmatically trigger video generation, manage projects, and retrieve video files without using the web UI. The API likely supports async job submission (returning a job ID for polling), webhook callbacks for completion notifications, and batch operations, enabling integration into custom workflows, CI/CD pipelines, or third-party applications.

Provides pre-built video templates (e.g., 'Product Demo', 'Customer Testimonial', 'Training Module') with pre-configured avatars, storyboard layouts, and styling that users can customize and reuse. The system likely stores templates as parameterized video generation configurations that can be cloned, modified, and saved, enabling teams to maintain brand consistency and reduce setup time for common video types.