Legal discovery is one of the most time-consuming parts of litigation and typically involves team of specialists combing through towers of documents. Fileread, a startup that uses large language learning models (LLMs) to create tools for faster and more efficient discovery, announced today it has raised $6 million in seed funding.
The round was led by Gradient Ventures, Google’s AI-focused fund, with participation from Soma Capital.
Fileread’s tools are meant to increase the chances of crucial information being found in the discovery process, at a faster speed. Co-founder Chan Koh told TechCrunch that while he was studying engineering at Caltech, his parents lost his childhood home during the housing crisis of 2008 and did not understand the law well enough to find relief.
“Witnessing my parents grapple with the shock of losing something they’d worked so hard to attain was incredibly painful,” he said. “After graduating, I was motivated to build something that could have aided my parents and others in similar situations.”
Fileread was founded in 2020 shortly after its team, led by Koh and co-founder and co-CEO Daniel Hu, began collaborating with the Deliberate Democracy Lab of Stanford University to analyze their deliberations. Freya Zhou joined then as COO and co-founder, and Fileread built its first LLM platform. This made them realize the power LLMs have in finding the right passages from enormous amounts of text and that legal discovery had similar problems to deliberations, but at much larger scale.
Fileread founders Chan Koh, Freya Zhou and Daniel Hu
For example, Fileread is currently being used on a case with more than a million documents, with only a team of 40 to 50 specialist reviewers. Fileread can help them save them by answering time-consuming queries. Users can ask Fileread anything related to the content of the documents uploaded to its platform. For example, if they ask “who was involved in the transactions,” Fileread returns a list of all possible answers highlighted in the original document.
Legal teams can safeguard against wrong answers because Fileread provides citations to each answer from its LLMs, which direct users to the original sources of truth that generated the LLM response in the first place.
Some other startups in the legal space include Casetext and Harvey. Koh said Fileread differentiates from Casetext because Casetext’s primary focus is on case research instead of discovery. Meanwhile, Harvey is focused on serving the broader legal services market.
Fileread’s new funding will be used on hiring, scaling its product and finding new ways to use LLMs for legal applications.
Backed by Gradient, Fileread use LLMs to make legal discovery more efficient by Catherine Shu originally published on TechCrunch