Show HN: I built an 11-LLM consensus engine to detect AI hallucination (github.com)

A developer has unveiled a comprehensive software kit, an 11-LLM consensus engine, designed to enhance AI applications by integrating 14 large language model (LLM) providers while ensuring compliance with the EU AI Act. Unlike typical offerings that rely on a single LLM, this boilerplate facilitates semantic consensus, allowing for more trustworthy outputs by cross-referencing multiple models. This capability is particularly significant in reducing the risk of AI hallucination, as it leverages consensus scoring on embeddings rather than purely lexical analysis, providing a more nuanced understanding of model agreements and disagreements. The kit also features numerous technical innovations crucial for developers, including self-evolving learning loops, optional audit trail generation, and the ability to manage operational costs effectively. Central to its design is a fail-closed gate system for high-risk actions and robust privacy measures for user data. Developers can deploy applications quickly, focusing on unique product features rather than infrastructure challenges, and benefit from various integrations and customizable options tailored for specific use cases such as legal compliance or AI assessments. With its extensive features and rapid deployment capabilities, this tool aims to streamline the complexity of AI software development and enhance model reliability, making it a notable addition to the AI/ML community.