Luke Kim, Founder and CEO of Liner – Interview Series

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 automatically generates citations in various formats, making it an invaluable resource for researchers, students, and professionals.

Can you tell us about your background and what inspired you to pursue entrepreneurship, especially in the field of AI and technology?

My entrepreneurial journey began with a desire to address real-world problems through technology. As an undergraduate, I was struck by how challenging it was to navigate and trust the abundance of information online. I was motivated to create a tool that streamlines the process and helps students discern between sources. What started as a highlighting tool, weeding through available information, over time developed into what Liner is today: an AI search that provides only the most reliable results. I was drawn to AI for its potential to transform how we process and interact with data. The opportunity to create meaningful solutions for students, like my younger self, continues to inspire 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 problem of information 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 impact, whether it’s highlighting important points or surfacing relevant sources. This project shaped Liner’s commitment to creating a seamless experience for users, and helping students and researchers weed through the excess noise on the internet.

What was the original vision behind Liner, and how has it evolved since its inception?

Liner began as a simple tool to help users highlight and save key parts of online content. The goal was to make it easier for users to focus on the most relevant information without being overwhelmed. Over time, we recognized that users needed more than a way to collect and sort information—they needed better ways to find it and discern its reliability. This realization guided Liner’s transformation into an AI search engine.

What were the major challenges you faced while transitioning Liner from a highlighting tool to an AI-driven search engine?

One of the most 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. Another challenge was integrating years of user-highlighted data into the AI’s training process while keeping the platform intuitive. Striking the right balance between technological innovation and a seamless user experience was essential but also incredibly rewarding.

By building Liner’s definition of “agent” from scratch, we were able to create a robust and stable framework for understanding what an agent really is. We then implemented a search agent that prioritized reliability and credibility. Given that our target audience represents the pinnacle of credibility-focused expectations, we needed a distinctive solution capable of addressing the most complex problems. Our strength lay in leveraging our proprietary datasets, the technical insights gained during the agent definition process, and our implementation expertise. Together, these elements became our most powerful tools for success.

Can you elaborate on how the integration of user-highlighted data enhances the accuracy and reliability of Liner’s AI search results?

User-highlighted data acts as a valuable layer of quality control, helping our LLM discern what other users find important and credible. By leveraging this curated data, we are able to 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 includes a citation, and users can filter out less reliable sources to ensure accuracy. As an additional measure, students can pull sources and view the original quoted text on their screen. Unlike tools designed for casual queries, Liner is purpose-built for students, academics, and researchers, helping users focus on in-depth learning and analysis instead of verifying facts. This commitment to trust and usability 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 differentiate 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 reduce hallucinations in its AI responses, and how 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 also build trust with the user.

Liner’s system is based on relevance (the relevance score 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 higher the factuality score.Since our product strongly encourages users to verify claims to ensure they are free from hallucinations, enhancing the factuality of our agent system is crucial. Ultimately, we observe a positive correlation between the factuality score and user retention.

What steps is Liner taking to build trust among users, especially those skeptical about relying on AI for critical information?

Building trust begins with transparency. Liner provides clear citations for every result, giving users the ability to verify the information themselves. Additionally, we rank sources based on reliability and allow users to engage directly with the original content. Continuous user education and open communication also play a role in demonstrating that AI, when designed responsibly, can be a dependable ally in education.

What trends do you think will shape the future of AI in academic research and professional knowledge retrieval?

AI will become increasingly personalized, adapting to the unique needs of each user and providing tailored insights. Transparency will be key, as users seek greater clarity about how AI processes information and delivers results. Advancements will also focus on 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 thinking, analysis, and innovation. This balance between efficiency and intellectual engagement will shape the future of academic and professional research.

Liner recently successfully raised a $29 million funding round. How will this investment help Liner grow, and what areas are you focusing on for expansion?

This funding enables us to advance our mission of improving AI in education. We're growing our global team and rolling out new features like Essay Mode, designed to help students refine their skills in writing, structuring, and formatting essays. We're also prioritizing partnerships with universities and professional organizations to reach more users and showcase the impact of AI-powered research tools. Recent collaborations with companies like ThetaLabs and Tako have expanded our capabilities. This investment highlights the growing need for dependable search solutions, and we're eager to build on this momentum.

Thank you for the great interview, readers who wish to learn more should visit Liner.

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