Customer-Facing Chatbots

Leveraging Content Intelligence, organizations can guarantee the integrity and excellence of the data utilized by Customer-facing Chatbots, ensuring that only cleansed and approved content is employed for external use.

The problem

Making sure responses are accurate, reliable, and compliant is essential when creating Chatbot solutions and especially so when they are Customer-facing. GenAI models are prone to hallucinations, which can result in non-compliant, factually incorrect or potentially harmful responses. Organizations must ensure that data accessed by GenAI is accurate, clean, and properly contextualized so that LLMs are grounded in verifiable knowledge sources.

The solution

With Content Intelligence, gain complete transparency and control over the data accessed and utilized by Chatbot systems for generating answers.

[ Steps to solution ]

Content Intelligence simplifies integration processes through its 15 pre-built connectors, allowing teams to seamlessly integrate content from various enterprise data sources into a unified semantic layer. Content Intelligence supports all common enterprise file formats as well as different data modalities such as images and tables. Maximize the value of your data no matter the source or type and without the need for manual information extraction procedures.

Content Intelligence assesses content quality and identifies potential risks such as conflicting, outdated and missing information, as well as scans for the presence of private or internal use only data. By leveraging Content Intelligence scores, proactively address content issues before they lead to inaccurate or non-compliant answers being presented to customers.

Content Intelligence adopts an API-first approach and provides thorough developer documentation and SDKs for Python and Javascript, facilitating integration with any Chatbot and Conversational AI system. Content Intelligence readily fits into your tech stack and enables you to constantly monitor the health of your data as well as integrate and analyze user feedback.