Interfaze

Developers building document, image, audio or data-extraction workflows need outputs they can trust and verify, not just plausible-sounding text. General purpose LLMs are not built for repeatable, production-grade extraction and do not natively provide confidence scores, bounding boxes, or guardrails against unsafe content. Teams end up building their own verification and safety layers around general models to get deterministic, rule-based results for tasks like ID or document parsing, medical memos, or government forms.

Interfaze offers two models, the flagship Interfaze 1 and the smaller open-weight, self-hostable Interfaze 1.1 Lite, accessed through official TypeScript and Python SDKs or a chat-completions-compatible API that works with frameworks like Vercel AI SDK and LangChain. Outputs include confidence scores and bounding boxes for each extracted field, letting developers build rule-based systems on top of the results. The models handle multimodal input (text, images, audio, file, and video), work across 100+ languages, and include configurable guardrails covering categories like violent crimes, hate, sexual content, and privacy. The architecture is described as a hybrid Mixture-of-Architecture combining specialized DNNs/CNNs with a transformer layer, aiming for higher accuracy and precision than general LLMs on deterministic tasks.