Vectara

Vectara is a developer of an AI-powered text search platform designed to give users semantic search capabilities. The platform uses advanced neural networks and generative AI to search textual data and offers features such as textual recall, multilingual support, and data security. Users can question their data and receive summarized answers, which can be further integrated into a user's application, knowledge base, website, chatbot, or helpdesk.

Vectara was founded in 2022 by Amr Awadallah, Amin Ahmad, and Tallat Shafaat and is headquartered in Palo Alto, California.

Vectara's platform is a cloud-native, API-driven, large language model (LLM)-powered search platform built to offer users website and application search. Using LLMs, the search allows users to use semantic search, and the platform can respond based on an understanding of implied and contextual meaning to help better answer users' questions. The platform can be applied without users needing to retrain, tune, add stop words, synonyms, knowledge graphs, or use ontology management to get the platform to work as needed.

The Vectara platform can be applied to any data ingested, including processing emails, social media comments, web pages, customer support tickets, messages, survey responses, tweets, and documents across an organization. This allows the platform to make recommendations, identify related content, generate question-and-answer responses, route queries, and analyze customer trends and preferences for users.

Generative AI allows users to converse with the Vectara platform to receive summarized answers to their questions based on their data, for either conversational chatbots, user-generated content, or research and analysis libraries. However, Vectara claims the platform uses a proprietary method to eliminate hallucinations (false information) in the generative AI's answers. To reduce the hallucinations, Vectara's grounded generation uses hybrid search and boolean exact match to find relevant products, support cases, and documents to answer a question; it uses simple summarization across pages and documents to best answer a user's questions; and only relies on facts and data provided by the organization to eliminate the potential for hallucinations.