Automatically Answer Complex Questions
from an Extensive KnowledgeBase
The Kore.ai XO Platform integrates with OpenAI technology to use generative AI capabilities. Simply upload documents and the platform will automatically read the data to answer customer queries without needing prior training.
With this capability, leverage your existing institutional documentation to develop and expand upon your virtual assistant’s knowledge base. Knowledge Graphs designed on the XO Platform with nodes, tags, and synonyms will equip your virtual assistant with all the right information to cover all possible matches.
The new intent-less model uses the power of large language models and generative AI technologies to eliminate manual intent creation and answer users’ questions contextually.
The new feature can take content from diverse sources like webpages, PDF files, and CMS pages. It uses a combination of intelligent algorithms to understand the document layout, semantic meaning of the content using vector-based embeddings, and OCR technologies to efficiently generate contextually relevant answers for your users’ questions without the need for manual FAQ creation. The feature also ensures up-to-date answers by auto-syncing the information from external sources.
Provide question-answer sets to relevant nodes in the hierarchy to deliver an intelligent FAQ experience to the users. Add synonyms and channel-specific responses.
Link dialog task to Knowledge Graph intent and provide an aha experience to your users by handling their FAQs involving complex conversations.
Construct a Knowledge Graph (KG) – a hierarchy with key domain terms, make it more natural by adding context-specific questions and their derivatives, synonyms, and ML models.
While working on large data sets, leverage the Kore.ai Knowledge Graph Generator from Github to generate an efficient knowledge graph.
In the real-world, users may provide incomplete or ambiguous inputs, which virtual assistants may fail to understand and provide incorrect responses.
The XO Platform advanced NLP capability in the form of a Taxonomy-based approach and built-in flows help engage the user in a multi-turn conversation to clarify user inputs and identify the right intent.
Leverage the powerful Annotation tool to annotate documents identifying the key sections of the content. Mark them as Header, Heading, Footer, Exclude or even Ignore page. It will help the Knowledge Graph engine to extract content efficiently and process it to deliver optimized results.
A careful analysis of the Knowledge Graph helps detect errors in your questions and the path associated with the same that hamper the user experience.
Knowledge Graph Diagnosis tool allows you to identify any inefficiencies in your knowledge graphs and suggest possible corrective actions