Luke Kim is the Founder and CEO of Liner, a cutting-edge AI-powered analysis instrument designed to streamline and improve the analysis course of, serving to customers full their duties 5.5 instances sooner. As an AI search engine, Liner gives filtered search outcomes for exact data and robotically 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 know-how?
My entrepreneurial journey started with a need to deal with real-world issues by means of know-how. As an undergraduate, I used to be struck by how difficult it was to navigate and belief the abundance of data on-line. I used to be motivated to create a instrument that streamlines the method and helps college students discern between sources. What began as a highlighting instrument, weeding by means of out there data, over time developed into what Liner is in the present day: an AI search that gives solely probably the most dependable outcomes. I used to be drawn to AI for its potential to rework how we course of and work together with information. The chance to create significant options for college students, 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 data overload. It confirmed me how a lot folks worth instruments that make discovering and organizing key data simpler. I realized that simplifying even one step of a workflow can have a big effect, whether or not it’s highlighting vital factors or surfacing related sources. This undertaking formed Liner’s dedication to making a seamless expertise for customers, and serving to college students and researchers weed by means of the surplus noise on the web.
What was the unique imaginative and prescient behind Liner, and the way has it developed since its inception?
Liner started as a easy instrument to assist customers spotlight and save key components of on-line content material. The purpose was to make it simpler for customers to give attention to probably the most related data with out being overwhelmed. Over time, we acknowledged that customers wanted greater than a option to gather and type data—they wanted higher methods to seek out it and discern its reliability. This realization guided Liner’s transformation into an AI search engine.
What had been the key challenges you confronted whereas transitioning Liner from a highlighting instrument to an AI-driven search engine?
Probably the most vital challenges was making certain that our AI might persistently ship dependable and correct outcomes. Educational analysis requires a excessive diploma of belief, and assembly these expectations was crucial. One other problem was integrating years of user-highlighted information into the AI’s coaching course of whereas preserving the platform intuitive. Putting the suitable steadiness between technological innovation and a seamless consumer expertise was important but additionally extremely rewarding.
By constructing Liner’s definition of “agent” from scratch, we had been in a position to create a sturdy and steady framework for understanding what an agent actually is. We then carried out a search agent that prioritized reliability and credibility. On condition that our target market represents the top of credibility-focused expectations, we wanted a particular answer able to addressing probably the most complicated issues. Our energy lay in leveraging our proprietary datasets, the technical insights gained throughout the agent definition course of, and our implementation experience. Collectively, these components grew to become our strongest instruments for achievement.
Are you able to elaborate on how the combination of user-highlighted information enhances the accuracy and reliability of Liner’s AI search outcomes?
Consumer-highlighted information acts as a helpful layer of high quality management, serving to our LLM discern what different customers discover vital and credible. By leveraging this curated information, we’re in a position to prioritize related and reliable data in our search outcomes. This method 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 an extra measure, college students can pull sources and consider the unique quoted textual content on their display. In contrast to instruments designed for informal queries, Liner is purpose-built for college students, teachers, and researchers, serving to customers give attention to in-depth studying and evaluation as an alternative of verifying information. This dedication to belief and value makes Liner a go-to instrument 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 means of partnerships, like a current one with Tako, which integrates information 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 scale back hallucinations in its AI responses, and the way does this influence consumer belief?
Decreasing 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 consumer.
Liner’s system relies on relevance (the relevance rating between agent-generated claims and reference passages) and factuality (which assesses how nicely 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. In the end, we observe a optimistic correlation between the factuality rating and consumer retention.
What steps is Liner taking to construct belief amongst customers, particularly these skeptical about counting on AI for crucial data?
Constructing belief begins with transparency. Liner gives clear citations for each end result, giving customers the flexibility to confirm the data themselves. Moreover, we rank sources based mostly on reliability and permit customers to have interaction immediately with the unique content material. Steady consumer training and open communication additionally play a task in demonstrating that AI, when designed responsibly, generally is a reliable ally in training.
What developments do you assume will form the way forward for AI in tutorial analysis {and professional} information retrieval?
AI will change into more and more customized, adapting to the distinctive wants of every consumer and offering tailor-made insights. Transparency will probably be key, as customers search better readability about how AI processes data and delivers outcomes. Developments may even give attention to addressing data 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 crucial pondering, evaluation, and innovation. This steadiness between effectivity and mental engagement will form the way forward for tutorial {and professional} analysis.
Liner not too long ago efficiently raised a $29 million funding spherical. How will this funding assist Liner develop, and what areas are you specializing in for enlargement?
This funding permits us to advance our mission of bettering AI in training. We’re rising our world staff 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 succeed in extra customers and showcase the influence of AI-powered analysis instruments. Latest 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.