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Comparative Analysis of AI Search Engines: Goover and Perplexity

GOOVER DAILY REPORT September 7, 2024
goover

TABLE OF CONTENTS

  1. Summary
  2. Introduction to Goover
  3. Introduction to Perplexity
  4. Technological Comparison
  5. User Experience and Interface
  6. Market Impact and Future Directions
  7. Conclusion

1. Summary

  • This report provides a comprehensive comparative analysis of two AI search engines: Goover and Perplexity. It examines their core technologies, functionalities, and market impacts. Goover, developed from Saltlux, utilizes Large Language Models (LLM) and Graph Retrieval-Augmented Generation (Graph RAG) to offer deep automated research capabilities. Perplexity, founded by former OpenAI employees, excels in conversational search, providing quick, reliable responses with clear source attribution. The comparison highlights Goover’s emphasis on creating comprehensive research tools and Perplexity’s strength in delivering fast, verified answers. This detailed examination helps in understanding their distinct roles and advancements in the AI search engine market.

2. Introduction to Goover

  • 2-1. Origin and Development

  • Goover is a startup that was established as a spin-off from Saltlux, a South Korean AI specialist company with over 10 years of research experience in artificial intelligence and generative AI. The company recently established its U.S. subsidiary in Silicon Valley and launched its services simultaneously in South Korea and the U.S. Goover aims to innovate within the global search service market, competing directly with AI search engines such as Perplexity.

  • 2-2. Core Technologies (LLM and Graph RAG)

  • The core technology of Goover includes the 'Ask Goover' feature, which utilizes Large Language Models (LLM) and Graph Retrieval-Augmented Generation (Graph RAG) technologies. This advanced AI agent can navigate the vast information available worldwide in multiple languages, offering users the most optimized answers along with clear source citations. Goover's AI, referred to as 'Connectome', learns from user behavior and preferences, enabling it to find necessary information efficiently and automatically generate in-depth reports.

  • 2-3. Bridging Research Automation

  • Goover emphasizes automated knowledge exploration, which is reflected in its name 'Go over', indicating a focus on deep research and analysis. Users can create 'Briefing Pages' for topics they wish to monitor. These pages compile relevant news, social media reactions, quotes, and information about related individuals or companies in a card news format. AI-generated custom reports and analysis widgets aim to save users time and enhance research efficiency. Additionally, Goover integrates social features by recommending similar user Briefing Pages for subscription and sharing through social media, thereby functioning as a socializing platform.

3. Introduction to Perplexity

  • 3-1. Origin and Development

  • Perplexity is an AI search engine startup founded by former employees of OpenAI. It has garnered significant attention following substantial investments from Nvidia and Jeff Bezos. This information highlights the startup's recent rise in prominence within the AI search engine landscape.

  • 3-2. AI-based Conversational Search

  • Perplexity operates as an AI-based conversational search engine capable of providing quick responses to complex queries while clearly presenting sources for the information. This design enables users to access reliable information efficiently, marking it as a potential alternative to traditional keyword-focused search engines such as Google. Through its 'Pages' feature, users can create personalized web pages to access and share tailored information easily.

  • 3-3. Market Position and Funding

  • The global search service market is estimated to be worth $120 billion. Perplexity's ability to attract investment from major industry figures underscores its competitive position against established players like Goover. Market expectations suggest that Perplexity may serve as a powerful alternative within this expanding market, particularly as it leverages advanced AI technologies and operational strategies.

4. Technological Comparison

  • 4-1. AI Models and Techniques

  • Goover's technology employs a blend of large language models (LLM) and graph-based search techniques, referred to as Graph RAG. This allows for sophisticated automated information retrieval and processing, enabling the AI to generate in-depth reports tailored to user queries. In contrast, Perplexity leverages advanced AI conversational capabilities, enabling quick responses to complex questions and providing reliable source citations, thereby enhancing user trust in the information provided.

  • 4-2. Information Accuracy and Source Attribution

  • Both Goover and Perplexity prioritize information accuracy. Goover uses AI to not only retrieve but also summarize and analyze data from a wide array of online sources, further enhancing the reliability of its automated reports. Perplexity ensures accuracy by delivering verified answers with clear source attributions, making it a trustworthy conversational search engine. This dual emphasis on accurate data sourcing is essential for users looking for dependable information.

  • 4-3. Customization and Security Features

  • Goover plans to introduce an enterprise version designed with enhanced customization and security features suitable for corporate clients. This includes on-premises solutions that maximize data security. Although the detailed security features were not highlighted specifically for Perplexity, its architecture is developed to efficiently handle user data, aiming for optimal performance while maintaining user privacy.

5. User Experience and Interface

  • 5-1. Personalized Information Pages

  • The 'Personalized Information Pages' feature allows users to create briefing pages for topics they wish to monitor or analyze closely. These pages display a variety of related content, including news, social media reactions, quotes, and information about relevant individuals and companies. This format, presented as card news, aims to deliver comprehensive insights efficiently. Both Goover and Perplexity utilize AI technologies to generate tailored reports and recommendations based on the user's interests, thus enhancing the overall research experience.

  • 5-2. User Interactions and Social Integration

  • Goover incorporates a 'Social Briefing' tab that allows users to receive recommendations on briefing pages of similar topics created by other users. Users can subscribe to these pages and share them on social media, extending their functionality beyond mere information provision to a socializing platform. This integration aids in fostering user engagement through collaborative information sharing. Additionally, both platforms support user interactions, enhancing the overall experience by allowing for personalized feedback and adjustments.

  • 5-3. Language Support and Accessibility

  • Both Goover and Perplexity are designed to be accessible in multiple languages, utilizing advanced AI models to ensure users receive relevant responses irrespective of their linguistic background. Perplexity, particularly, emphasizes optimizing language support in its conversational AI searches, presenting information sourced from worldwide data effectively. This feature plays a critical role in making AI-driven information retrieval services inclusive and user-friendly.

6. Market Impact and Future Directions

  • 6-1. Current Market Impacts

  • Goover, a new AI search service, was launched simultaneously in South Korea and the United States on September 3. This launch positions Goover as a competitor to Perplexity, marking a significant entry into the $120 billion global search service market. Perplexity has attracted substantial investments from prominent figures, including Nvidia and Amazon founder Jeff Bezos. It is known for delivering quick and accurate responses to complex questions, thereby challenging traditional keyword-centric search models like Google's. Goover aims to automate the entire knowledge exploration process, enhancing deep research capabilities, and is developed by a startup formed from a decade of AI and Generative AI research.

  • 6-2. Feedback from Beta Testing

  • The beta version of Goover has been made available for users after registration on its official website. The company plans to improve the completeness of its official service based on the feedback gathered during the beta testing period. Joshua Bae, the head of the U.S. subsidiary, highlighted that Goover integrates the latest language models and automatic document generation capabilities with an experience for information sharing on social media, addressing the information overload problem commonly faced by knowledge professionals.

  • 6-3. Potential Developments and Innovations

  • Looking ahead, Goover plans to offer customized solutions for enterprise customers while maximizing security through on-premises and appliance versions. This indicates a strategic direction toward catering to higher-end market segments with specialized needs. Goover also features functionality such as briefing pages for topic monitoring, allowing users to view news, social media reactions, citations, and information related to entities or individuals, enhancing user interaction and insight generation.

7. Conclusion

  • The report elucidates the unique positions of Goover and Perplexity within the AI search engine landscape. Goover leverages LLM and Graph RAG technologies to provide robust automated deep research capabilities, making it a valuable tool for in-depth information retrieval and analysis. The Connectome AI further personalizes the user experience by learning preferences and interests. On the other hand, Perplexity’s strengths lie in its AI-based conversational search model, ensuring quick and accurate response to complex queries with transparent source attribution, which boosts user trust. The importance of these findings lies in showcasing how both platforms cater to different user needs—Goover for exhaustive research and Perplexity for efficient information retrieval. The report also mentions limitations, such as the need for further development of Goover’s enterprise solutions and security features for broader adoption. Future research could delve into long-term user engagement and the evolving market dynamics influenced by these technologies. Practical applications of these findings include enhancing search functionalities in diverse contexts, from academic research to enterprise information systems.

8. Glossary

  • 8-1. Goover [AI Search Engine]

  • Goover, developed by a Saltlux spin-off, provides automated deep research capabilities using LLM and Graph RAG technologies. It supports personalized information retrieval and integrates social media interactions.

  • 8-2. Perplexity [AI Search Engine]

  • Perplexity, an AI-based conversational search engine established by former OpenAI employees, offers fast responses and clear source attributions. It recently garnered significant investments from NVIDIA and Jeff Bezos.

  • 8-3. LLM (Large Language Model) [Technology]

  • LLM is used by Goover for advanced language understanding and generation, enabling comprehensive information retrieval across diverse languages.

  • 8-4. Graph RAG (Graph-based Retrieval-Augmented Generation) [Technology]

  • Graph RAG, employed by Goover, enhances document retrieval and generation accuracy, ensuring optimized answers based on extensive web data.

  • 8-5. Connectome [AI Model]

  • Connectome in Goover refers to the AI brain that learns user preferences and interests to deliver relevant and personalized information via automated reports.

9. Source Documents