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 instances sooner. As an AI search engine, Liner offers filtered search outcomes for exact data and routinely 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 discipline of AI and expertise?
My entrepreneurial journey started with a need to handle 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 accessible data, over time developed into what Liner is at this time: 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 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 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 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 strategy to accumulate and kind data—they wanted higher methods to search out it and discern its reliability. This realization guided Liner’s transformation into an AI search engine.
What had been the most important challenges you confronted whereas transitioning Liner from a highlighting software to an AI-driven search engine?
One of the vital challenges was guaranteeing that our AI might constantly 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 conserving the platform intuitive. Placing the best steadiness between technological innovation and a seamless person expertise was important but in addition extremely rewarding.
By constructing Liner’s definition of “agent” from scratch, we had been capable of 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. Provided that our audience represents the head of credibility-focused expectations, we wanted a particular answer able to addressing probably the most advanced issues. Our energy lay in leveraging our proprietary datasets, the technical insights gained in the course of the agent definition course of, and our implementation experience. Collectively, these parts grew to become our strongest instruments for fulfillment.
Are you able to elaborate on how the mixing of user-highlighted information enhances the accuracy and reliability of Liner’s AI search outcomes?
Consumer-highlighted information acts as a beneficial 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 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 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 examine the unique quoted textual content on their display screen. Not like instruments designed for informal queries, Liner is purpose-built for college students, lecturers, 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 way of partnerships, like a latest one with Tako, which integrates data visualization instruments to current advanced 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 affect 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 in addition construct belief with the person.
Liner’s system is predicated 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 data themselves. Moreover, we rank sources primarily based on reliability and permit customers to interact instantly 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 tendencies do you suppose will form the way forward for AI in educational analysis {and professional} data retrieval?
AI will develop into more and more personalised, adapting to the distinctive wants of every person and offering tailor-made insights. Transparency will likely be key, as customers search larger readability about how AI processes data and delivers outcomes. Developments may also give attention to addressing data overload and streamlining analysis instruments. By automating repetitive duties like information gathering and synthesis, AI will pace up the early phases of analysis, enabling researchers to focus extra on vital pondering, evaluation, and innovation. This steadiness between effectivity and mental engagement will form the way forward for educational {and professional} analysis.
Liner lately 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 international group 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 affect 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.