Knowledge graph database startup Stardog Union is launching a new, “hallucination-free” version of its enterprise-grade chatbot Voicebox, aimed at high-stakes industries. Voicebox can be thought of as an “enterprise answer engine” that’s linked to an organization’s internal data, allowing it to respond to knowledge worker’s questions with extreme accuracy in real time. Stardog said it’s targeted at organizations in the most heavily regulated industries, such as financial services, healthcare and defense, enabling them to ask complex questions and receive answers that are based on their own data and fully traceable. The chatbot leverages Stardog’s pioneering knowledge graph platform, which is a flexible and reusable data layer that can access information from across various disparate systems, including siloed databases and applications. It unites data from across the entire organization to power data analytics and other big data initiatives. Stardog says Voicebox is essentially an additional AI layer built atop of its knowledge graph, powered by multiple agents that collaborate behind the scenes on tasks such as data discovery, integration, modeling and mapping. The idea is to provide knowledge workers with a user-controlled, self-service analytics experience that’s completely accessible via natural language commands. It’s supposed to enable everyone within an organization to carry out their own analytics operations and dig up better business insights, the company said. The company’s knowledge graph utilizes what Stardog says is a “Safety RAG architecture,” which it claims is safer than traditional retrieval-augmented generation because it’s designed to ensure no hallucinations can slip through the cracks. Stardog says Safety RAG is also more expressive, because its knowledge graph allows it to build a more complete data environment, expanding its reach from unstructured data only to include traditional databases and other structured data types. According to Stardog, Voicebox will never generate false responses. Instead, it will simply admit when it doesn’t know how to answer a specific question. When this happens, it will ask users to provide examples of “competency questions” so it can direct its agents to perform the necessary integrations, modeling and mapping to try and answer the original question.