KGB: Grounded OCR and RAG on AMD

KGB provides two GPU inference containers for the AMD AI League: optical character recognition and document-grounded question answering. Both preserve the required AMD ROCm PyTorch base layers and include a pinned Qwen3-VL-8B-Instruct model, so inference does not need a network connection or a hosted API. The OCR service reads license plates and road signs from PNG, JPEG and TIFF inputs. It keeps the model resident while each evaluator invocation writes its own JSON result. The RAG service indexes a mixed corpus once, reads PDF, Word, spreadsheet, CSV, source-code and image content, and returns a short answer with corpus-relative citations. It validates quoted evidence and returns an empty answer when support is missing. The public repository includes behavioral tests for parsing, independent client processes, output contracts and evidence verification. Both images build successfully and resolve anonymously from the registry. Native Radeon evaluation passed 10 OCR illustrations, 32 synthetic degraded OCR cases and all 10 RAG sample questions with exact citation sets. The required Instinct/container environment and private evaluation set have not been tested. Public examples and synthetic cases are development checks, not a claim about hidden-set scores.