Hebo AI helps medium-sized organisations manage conversational agents through a low-code suite. It uses a optimised LLM+RAGs+Actions implementation that is able to handle thousands of customer conversations per day. Hebo addresses 2 key problems: 1) As conversational agents communicate independently with thousands of customers / partners / employees, we identified that trust in the quality of conversations is a huge hurdle to the final adoption / actual launch. 2) Like humans need to be “trained-on-the-job”, agents need the same. This requires quick iteration over the knowledge and behaviours of the agents. Today this is a very time-consuming process as it always involves tech teams. Our solution to these problems is a toolchain that allows business stakeholders to curate knowledge, validate conversation quality and monitor agents deployed to production. Think of it as a Github + TestRails + Circle CI + Data Dog for conversational agents. But not on a technical level, on a business level. What Hebo AI will provide: - Pre-Configured AI Agent: Ready-to-use agents with RAG, memory, and chat handling while allowing for customization (e.g. LLM choice, connect to existing agent, ..). - Curated Knowledge Management: A Notion-like interface to structure and refine knowledge, connect to external data and test behaviour via sample conversations. - Automated Performance Monitoring: Real-time analytics, batch evaluations, and version comparisons to track and improve AI interactions. Hebo AI ensures that AI agents perform reliably, align with business goals, and deliver trustworthy interactions at scale. Hebo AI helps medium-sized organisations manage conversational agents through a low-code suite. It uses a optimised LLM+RAGs+Actions implementation that is able to handle thousands of customer conversations per day. Hebo addresses 2 key problems: 1) As conversational agents communicate independently with thousands of customers / partners / employees, we identified that trust in the quality of conversations is a huge hurdle to the final adoption / actual launch. 2) Like humans need to be “trained-on-the-job”, agents need the same. This requires quick iteration over the knowledge and behaviours of the agents. Today this is a very time-consuming process as it always involves tech teams. Our solution to these problems is a toolchain that allows business stakeholders to curate knowledge, validate conversation quality and monitor agents deployed to production. Think of it as a Github + TestRails + Circle CI + Data Dog for conversational agents. But not on a technical level, on a business level. What Hebo AI will provide: - Pre-Configured AI Agent: Ready-to-use agents with RAG, memory, and chat handling while allowing for customization (e.g. LLM choice, connect to existing agent, ..). - Curated Knowledge Management: A Notion-like interface to structure and refine knowledge, connect to external data and test behaviour via sample conversations. - Automated Performance Monitoring: Real-time analytics, batch evaluations, and version comparisons to track and improve AI interactions. Hebo AI ensures that AI agents perform reliably, align with business goals, and deliver trustworthy interactions at scale.
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