Luke Kim is the Founder and CEO of Liner, a cutting-edge AI-powered research tool designed to streamline and enhance the research process, helping users complete their tasks 5.5 times faster. As an AI search engine, Liner provides filtered search results for precise information and mechanically generates citations in various formats, making it a useful resource for researchers, students, and professionals.
Are you able to tell us about your background and what inspired you to pursue entrepreneurship, especially in the sphere of AI and technology?
My entrepreneurial journey began with a desire to handle real-world problems through technology. As an undergraduate, I used to be struck by how difficult it was to navigate and trust the abundance of knowledge online. I used to be motivated to create a tool that streamlines the method and helps students discern between sources. What began as a highlighting tool, weeding through available information, over time developed into what Liner is today: an AI search that gives only essentially the most reliable results. I used to be drawn to AI for its potential to rework how we process and interact with data. The chance to create meaningful solutions for college kids, like my younger self, continues to encourage me.
How did your experience with the browser extension you built during your university days shape the vision for Liner?
The Liner highlighter browser extension was my first real dive into solving the issue of knowledge overload. It showed me how much people value tools that make finding and organizing key information easier. I learned that simplifying even one step of a workflow can have a big effect, whether it’s highlighting details or surfacing relevant sources. This project shaped Liner’s commitment to making a seamless experience for users, and helping students and researchers weed through the surplus noise on the web.
What was the unique vision behind Liner, and the way has it evolved since its inception?
Liner began as an easy tool to assist users highlight and save key parts of online content. The goal was to make it easier for users to deal with essentially the most relevant information without being overwhelmed. Over time, we recognized that users needed greater than a strategy to collect and type information—they needed higher ways to search out it and discern its reliability. This realization guided Liner’s transformation into an AI search engine.
What were the key challenges you faced while transitioning Liner from a highlighting tool to an AI-driven search engine?
One of the vital significant challenges was ensuring that our AI could consistently deliver reliable and accurate results. Academic research requires a high degree of trust, and meeting those expectations was critical. One other challenge was integrating years of user-highlighted data into the AI’s training process while keeping the platform intuitive. Striking the correct balance between technological innovation and a seamless user experience was essential but in addition incredibly rewarding.
By constructing Liner’s definition of “agent” from scratch, we were capable of create a strong and stable framework for understanding what an agent really is. We then implemented a search agent that prioritized reliability and credibility. Provided that our target market represents the head of credibility-focused expectations, we would have liked a particular solution able to addressing essentially the most complex problems. Our strength lay in leveraging our proprietary datasets, the technical insights gained throughout the agent definition process, and our implementation expertise. Together, these elements became our strongest tools for achievement.
Are you able to elaborate on how the mixing of user-highlighted data enhances the accuracy and reliability of Liner’s AI search results?
User-highlighted data acts as a precious layer of quality control, helping our LLM discern what other users find vital and credible. By leveraging this curated data, we’re capable of prioritize relevant and trustworthy information in our search results. This approach ensures that users get precise and actionable insights while avoiding irrelevant or low-quality content.
How does Liner differentiate itself from other AI search tools like ChatGPT or Perplexity?
Liner stands out by prioritizing reliability and transparency. Every search result features a citation, and users can filter out less reliable sources to make sure accuracy. As a further measure, students can pull sources and examine the unique quoted text on their screen. Unlike tools designed for casual queries, Liner is purpose-built for college kids, academics, and researchers, helping users deal with in-depth learning and evaluation as a substitute of verifying facts. This commitment to trust and value makes Liner a go-to tool for over 10 million users, including students at universities like UC Berkeley, USC, University of Michigan, and Texas A&M. Liner continues to distinguish itself through partnerships, like a recent one with Tako, which integrates knowledge visualization tools to present complex data in a more accessible and interactive format, empowering users to dive deeper into their research.
What measures does Liner take to cut back hallucinations in its AI responses, and the way does this impact user trust?
Reducing hallucinations requires anchoring AI-generated responses to verifiable sources. Liner achieves this by cross-referencing its results with academic papers, government databases, and other trusted repositories. Our Source Filtering System further allows users to exclude unreliable content, providing an added layer of quality assurance. These steps not only minimize errors but in addition construct trust with the user.
Liner’s system is predicated on relevance (the relevance rating between agent-generated claims and reference passages) and factuality (which assesses how well the agent-generated claims are supported by the reference passages). The more supportive the passage, the upper the factuality rating.Since our product strongly encourages users to confirm claims to make sure they’re free from hallucinations, enhancing the factuality of our agent system is crucial. Ultimately, we observe a positive correlation between the factuality rating and user retention.
What steps is Liner taking to construct trust amongst users, especially those skeptical about counting on AI for critical information?
Constructing trust begins with transparency. Liner provides clear citations for each result, giving users the flexibility to confirm the knowledge themselves. Moreover, we rank sources based on reliability and permit users to have interaction directly with the unique content. Continuous user education and open communication also play a task in demonstrating that AI, when designed responsibly, could be a dependable ally in education.
What trends do you think that will shape the long run of AI in academic research and skilled knowledge retrieval?
AI will turn out to be increasingly personalized, adapting to the unique needs of every user and providing tailored insights. Transparency can be key, as users seek greater clarity about how AI processes information and delivers results. Advancements will even deal with addressing information overload and streamlining research tools. By automating repetitive tasks like data gathering and synthesis, AI will speed up the early stages of research, enabling researchers to focus more on critical considering, evaluation, and innovation. This balance between efficiency and mental engagement will shape the long run of educational and skilled research.
Liner recently successfully raised a $29 million funding round. How will this investment help Liner grow, and what areas are you specializing in for expansion?
This funding enables us to advance our mission of improving AI in education. We’re growing our global team and rolling out recent features like Essay Mode, designed to assist students refine their skills in writing, structuring, and formatting essays. We’re also prioritizing partnerships with universities and skilled organizations to achieve more users and showcase the impact of AI-powered research tools. Recent collaborations with corporations like ThetaLabs and Tako have expanded our capabilities. This investment highlights the growing need for dependable search solutions, and we’re desirous to construct on this momentum.
