Brainiall Image

Remove the background from an image. Uses Brainiall Cutout engine segmentation to precisely separate foreground from background. Returns a base64-encoded image with transparent background (PNG) or white background (WebP). Sub-500ms latency on GPU. Args: image_base64: Base64-encoded image data (PNG, JPEG, or WebP). output_format: Output format -- 'png' (with transparency) or 'webp'. Retu

Upscale image resolution with the Brainiall image-upscaling engine. Enhances image resolution by 2x or 4x with the GPU-accelerated Brainiall image-upscaling engine super-resolution. Processes in tiles (256x256) to manage VRAM. Maximum output dimension: 8192x8192. Args: image_base64: Base64-encoded image data (PNG, JPEG, or WebP). scale: Upscale factor -- 2 or 4 (default: 4). Returns:

Restore and enhance faces in an image with the Brainiall face-restoration engine. Detects all faces via RetinaFace, restores quality (fixes blur, noise, compression artifacts), and pastes them back. Optionally enhances the background with the Brainiall image-upscaling engine. GPU-accelerated, sub-3s latency. Args: image_base64: Base64-encoded image data containing faces (PNG, JPEG, WebP).

Check health status of Image API services and loaded models. Returns: dict with keys: - status (str): 'healthy' or error state - models (dict): Loaded model status per capability - version (str): API version

Turn a document image into structured fields. doc_type picks the schema (receipt/invoice/id/contract/form/generic). A page with no readable text returns an error rather than a guess. Returns: dict with keys: doc_type (str), fields (dict — null for any value not present), text (str — the recognised plain text).

Ask a natural-language question about a document image; returns a grounded answer plus the supporting line. Returns found:false rather than guessing when the document doesn't contain the answer. Returns: dict with keys: answer (str|null), found (bool), supporting_text (str|null), text (str).

Reconstruct every table in a document image into headers and rows. Returns: dict with keys: table_count (int), tables (list of {title, headers, rows, row_count, column_count}); [] if there are no tables.

Return the document as structured Markdown (headings, tables, lists, code blocks, math). Brainiall Doc Layout engine. The single API for converting documents to LLM-friendly format.

Run a multi-skill enrichment pipeline over a document image or text in one call. Brainiall Skillsets engine. Returns per-skill outputs ready for indexing or RAG.

Multimodal extraction. Send an image, text, or both; define your schema of fields; get structured JSON. Brainiall Content Understanding engine. Unified multimodal field extraction over images and text.