Luke Kim is the Founder and CEO of Liner, a cutting-edge AI-powered analysis software designed to streamline and improve the analysis course of, serving to customers full their duties 5.5 occasions quicker. As an AI search engine, Liner offers filtered search outcomes for exact info and mechanically generates citations in varied codecs, making it a useful useful resource for researchers, college students, and professionals.
Are you able to inform us about your background and what impressed you to pursue entrepreneurship, particularly within the area of AI and expertise?
My entrepreneurial journey started with a want to deal with real-world issues by expertise. As an undergraduate, I used to be struck by how difficult it was to navigate and belief the abundance of knowledge on-line. I used to be motivated to create a software that streamlines the method and helps college students discern between sources. What began as a highlighting software, weeding by obtainable info, over time developed into what Liner is at present: an AI search that gives solely essentially the most dependable outcomes. I used to be drawn to AI for its potential to remodel how we course of and work together with information. The chance to create significant options for college kids, like my youthful self, continues to encourage me.
How did your expertise with the browser extension you constructed throughout your college days form the imaginative and prescient for Liner?
The Liner highlighter browser extension was my first actual dive into fixing the issue of knowledge overload. It confirmed me how a lot individuals worth instruments that make discovering and organizing key info simpler. I discovered that simplifying even one step of a workflow can have a big effect, whether or not it’s highlighting necessary factors or surfacing related sources. This mission formed Liner’s dedication to making a seamless expertise for customers, and serving to college students and researchers weed by the surplus noise on the web.
What was the unique imaginative and prescient behind Liner, and the way has it advanced since its inception?
Liner started as a easy software to assist customers spotlight and save key elements of on-line content material. The aim was to make it simpler for customers to give attention to essentially the most related info with out being overwhelmed. Over time, we acknowledged that customers wanted greater than a solution to accumulate and kind info—they wanted higher methods to search out it and discern its reliability. This realization guided Liner’s transformation into an AI search engine.
What have been the most important challenges you confronted whereas transitioning Liner from a highlighting software to an AI-driven search engine?
One of the important challenges was guaranteeing that our AI might persistently ship dependable and correct outcomes. Tutorial analysis requires a excessive diploma of belief, and assembly these expectations was essential. One other problem was integrating years of user-highlighted information into the AI’s coaching course of whereas retaining the platform intuitive. Placing the suitable stability between technological innovation and a seamless person expertise was important but additionally extremely rewarding.
By constructing Liner’s definition of “agent” from scratch, we have been capable of create a strong and secure framework for understanding what an agent actually is. We then applied a search agent that prioritized reliability and credibility. Provided that our target market represents the top of credibility-focused expectations, we would have liked a particular resolution able to addressing essentially the most complicated issues. Our power lay in leveraging our proprietary datasets, the technical insights gained throughout the agent definition course of, and our implementation experience. Collectively, these components turned our strongest instruments for fulfillment.
Are you able to elaborate on how the combination of user-highlighted information enhances the accuracy and reliability of Liner’s AI search outcomes?
Person-highlighted information acts as a precious layer of high quality management, serving to our LLM discern what different customers discover necessary and credible. By leveraging this curated information, we’re capable of prioritize related and reliable info in our search outcomes. This strategy ensures that customers get exact and actionable insights whereas avoiding irrelevant or low-quality content material.
How does Liner differentiate itself from different AI search instruments like ChatGPT or Perplexity?
Liner stands out by prioritizing reliability and transparency. Each search end result features a quotation, and customers can filter out much less dependable sources to make sure accuracy. As a further measure, college students can pull sources and examine the unique quoted textual content on their display screen. In contrast to instruments designed for informal queries, Liner is purpose-built for college kids, teachers, and researchers, serving to customers give attention to in-depth studying and evaluation as a substitute of verifying information. This dedication to belief and usefulness makes Liner a go-to software for over 10 million customers, together with college students at universities like UC Berkeley, USC, College of Michigan, and Texas A&M. Liner continues to distinguish itself by partnerships, like a current one with Tako, which integrates data visualization instruments to current complicated information in a extra accessible and interactive format, empowering customers to dive deeper into their analysis.
What measures does Liner take to cut back hallucinations in its AI responses, and the way does this influence person belief?
Lowering hallucinations requires anchoring AI-generated responses to verifiable sources. Liner achieves this by cross-referencing its outcomes with tutorial papers, authorities databases, and different trusted repositories. Our Supply Filtering System additional permits customers to exclude unreliable content material, offering an added layer of high quality assurance. These steps not solely reduce errors but additionally construct belief with the person.
Liner’s system relies on relevance (the relevance rating between agent-generated claims and reference passages) and factuality (which assesses how properly the agent-generated claims are supported by the reference passages). The extra supportive the passage, the upper the factuality rating.Since our product strongly encourages customers to confirm claims to make sure they’re free from hallucinations, enhancing the factuality of our agent system is essential. Finally, we observe a optimistic correlation between the factuality rating and person retention.
What steps is Liner taking to construct belief amongst customers, particularly these skeptical about counting on AI for essential info?
Constructing belief begins with transparency. Liner offers clear citations for each end result, giving customers the power to confirm the knowledge themselves. Moreover, we rank sources based mostly on reliability and permit customers to have interaction straight with the unique content material. Steady person training and open communication additionally play a task in demonstrating that AI, when designed responsibly, is usually a reliable ally in training.
What traits do you assume will form the way forward for AI in tutorial analysis {and professional} data retrieval?
AI will grow to be more and more personalised, adapting to the distinctive wants of every person and offering tailor-made insights. Transparency will probably be key, as customers search larger readability about how AI processes info and delivers outcomes. Developments may even give attention to addressing info overload and streamlining analysis instruments. By automating repetitive duties like information gathering and synthesis, AI will velocity up the early phases of analysis, enabling researchers to focus extra on essential considering, evaluation, and innovation. This stability between effectivity and mental engagement will form the way forward for tutorial {and professional} analysis.
Liner just lately efficiently raised a $29 million funding spherical. How will this funding assist Liner develop, and what areas are you specializing in for growth?
This funding permits us to advance our mission of bettering AI in training. We’re rising our world workforce and rolling out new options like Essay Mode, designed to assist college students refine their abilities in writing, structuring, and formatting essays. We’re additionally prioritizing partnerships with universities {and professional} organizations to achieve extra customers and showcase the influence of AI-powered analysis instruments. Current collaborations with corporations like ThetaLabs and Tako have expanded our capabilities. This funding highlights the rising want for reliable search options, and we’re keen to construct on this momentum.
Thanks for the good interview, readers who want to study extra ought to go to Liner.
