Help Hive_AI Assistant
by GAVS Technologies
Transforms documents into a knowledge source for instant, accurate natural language answers
Help Hive is an AI-powered chatbot designed to enable organizations to upload documents and instantly retrieve accurate answers through natural language queries. This solution addresses challenges like query overload, inefficient response times, fragmented information, and gaps in knowledge sharing that many industries face. By providing a user-friendly document upload portal and leveraging a RAG (Retrieval Augmented Generation) powered search engine, Help Hive creates a customer-specific knowledge base that delivers comprehensive and timely responses.
The chatbot's key features include its ability to understand natural language, provide human-like answers, and offer easy plug-and-play integration. It is powered by Large Language Models (LLMs) and facilitates on-prem data residency, promotes self-service, and accelerates response times, ultimately closing knowledge gaps within organizations.
Help Hive's benefits span across various use cases and industries. For patients, it streamlines the retrieval of data from Policy and FAQ documents, while for employee self-help, it efficiently accesses information within HR policy documents. In sectors like BFSI (Banking, Financial Services, and Insurance), it aids in due diligence by quickly extracting key information from legal documents. The healthcare sector benefits from Help Hive by using it for patient self-help agents to answer patient queries and for healthcare administrators to manage hospital and insurance policies. Logistics and procurement managers can employ Help Hive for supplier contract management and vendor contract analysis, respectively. Even medical researchers and editors can utilize Help Hive to search for relevant studies and verify information in medical publications.
The operational flow of Help Hive involves several key steps. The admin uploads documents, which are then converted into embeddings and stored in a Vector Database. User queries are sent to the backend via API, and relevant data is fetched from the Vector Database and passed to the LLM to generate a response. This process ensures that users receive instant information access