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 sooner. As an AI search engine, Liner offers filtered search outcomes for exact data and robotically generates citations in numerous 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 discipline of AI and expertise?
My entrepreneurial journey started with a need to deal with real-world issues by way of expertise. 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 software that streamlines the method and helps college students discern between sources. What began as a highlighting software, weeding by way of obtainable data, over time developed into what Liner is immediately: an AI search that gives solely probably 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 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 individuals worth instruments that make discovering and organizing key data simpler. I discovered 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 mission formed Liner’s dedication to making a seamless expertise for customers, and serving to college students and researchers weed by way of 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 deal with probably the most related data with out being overwhelmed. Over time, we acknowledged that customers wanted greater than a approach to gather and kind 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 foremost challenges you confronted whereas transitioning Liner from a highlighting software to an AI-driven search engine?
Probably the most important 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 vital. One other problem was integrating years of user-highlighted information into the AI’s coaching course of whereas preserving the platform intuitive. Putting the appropriate 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 had been in a position to create a sturdy and steady 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 head of credibility-focused expectations, we would have liked a particular answer able to addressing probably 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?
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 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 outcome 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 look at the unique quoted textual content on their display screen. In contrast to instruments designed for informal queries, Liner is purpose-built for college students, lecturers, and researchers, serving to customers deal with in-depth studying and evaluation as an alternative of verifying details. 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 way of partnerships, like a latest 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 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 educational 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 effectively 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 person retention.
What steps is Liner taking to construct belief amongst customers, particularly these skeptical about counting on AI for vital data?
Constructing belief begins with transparency. Liner offers clear citations for each outcome, giving customers the flexibility to confirm the knowledge themselves. Moreover, we rank sources primarily based on reliability and permit customers to interact immediately with the unique content material. Steady person schooling and open communication additionally play a job in demonstrating that AI, when designed responsibly, is usually a reliable ally in schooling.
What tendencies do you suppose will form the way forward for AI in educational analysis {and professional} information retrieval?
AI will change into more and more personalised, adapting to the distinctive wants of every person and offering tailor-made insights. Transparency shall be key, as customers search higher readability about how AI processes data and delivers outcomes. Developments may even deal with addressing data overload and streamlining analysis instruments. By automating repetitive duties like information gathering and synthesis, AI will velocity up the early levels of analysis, enabling researchers to focus extra on vital considering, evaluation, and innovation. This stability between effectivity and mental engagement will form the way forward for educational {and professional} analysis.
Liner lately successfully raised a $29 million funding round. How will this funding assist Liner develop, and what areas are you specializing in for growth?
This funding allows us to advance our mission of bettering AI in schooling. We’re rising our world crew and rolling out new options like Essay Mode, designed to assist college students refine their expertise 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 firms 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 be taught extra ought to go to Liner.