We can now build drastically higher quality search because we can use LLMs in algorithms that mimic a human's systematic research process, instead of just roughly recommending results based on semantic embeddings or term frequency.
We built a deep search LLM pipeline that takes a few minutes to carefully search all the scientific literature. You describe your complex goal, as you would to a colleague. Then, we carefully search 200M+ papers. We classify the preliminary results with GPT-4. We then adapt the search goals based on relevant/irrelevant papers uncovered and continue searching, repeating in a scalable, structured exploration. Because of the classification accuracy, we can track this process statistically to predict what fraction of relevant papers have been discovered so far at any point, and know when the search is complete. There's more explanation of techniques/benchmarks in the whitepaper on our homepage.
We want to optimize the workflow for researchers in ML, biotech, medicine, etc, and would love critical feedback and suggestions. One major challenge is getting users to accurately describe their search goal, including everything implicitly in their head (instead of keyword phrasing). Another is how to differentiate what's happening behind the scenes, and manage expectations on timing (it's ~3-6 minutes). Also, of course, how to optimally present the results.
You say "try it now", but then link to a sign-up, and you don't have any social sign-in, so I can't just click a butt on and go.
If you look at elicit.com, look at their branding, the quality of their design, then look at your competing site. You need to up your game to get trust.
I'm assuming the reason you don't want to just have an open search is due to the cost of running searches, but what's the cost of nobody using it? How can you provide examples at least that showcase what you can do?
WRT the name of your name, the first thing that came to mind is undermine, which is not a positive connection to research.
I hope you can take this as constructive feedback. Like I said, I haven't even tried elicit yet, I can't remember what the other competitor in this space was.
But also, here's a bonus. Emmitt Shear just posted on twitter looking for quality research on reaction time. I know of at least one paper on slow-wave enhancement for deep sleep (CLAS, PTAS) and a secondary finding was on slow-wave sleep. I said I'd get back to him with the link, but maybe you can do even better and show us what your product can do. What's the best research into reaction time? Is there something other than Clare Anderson's paper on slow-wave sleep and reaction time?