Streamlined legal workflows with advanced AI solutions

6 weeks

from idea to MVP

$3 million

raised on total funding


San Francisco / New York


Product discovery

UX/UI design

Product management



Based in California, Tome is an innovative legal tech startup with a game-changing product: an application that harnesses the power of Artificial Intelligence and Large Language Models to distill complex investment contracts into simple, helpful, and data-rich summaries.

Stephen Trusheim, CEO, is an AI engineer by background. He transitioned into VC funds and later became the Partner and head of Operations at SignalFire. Nadia Dugal was a leader at Flourish Ventures, the Omidyar Network, and 500 Startups. She is also an educator on venture deals for Berkeley Law and the National Venture Capital Association.


Through its AI-powered application, Tome faced the challenge of efficiently distilling complex investment contracts into simple, data-rich summaries. However, their ecosystem lacked an internal tool for lawyers to effectively view, review, and perform additional operations on these contracts, hindering the legal team's ability to provide timely clarifications and modifications to AI-generated summaries.

Our Approach & Solution

Sudolabs was brought in to develop an internal tool tailored for Tome's legal team, facilitating additional contract assessment and modification. This tool aimed to streamline the legal team's workflow, enabling them to address discrepancies in the AI-generated contract translations quickly.

The project kicked off with a two-week product discovery sprint, closely involving Tome's engineers, lawyers, and CEO to understand their needs and expectations. This collaboration led to dynamic four-week development sprints, culminating in the creation of a Minimum Viable Product (MVP). The development process featured rapid feedback cycles, weekly releases, and active involvement from the tech team to ensure the solution effectively met Tome’s needs.


The partnership between Tome and Sudolabs blossomed into a year-long engagement, highlighting the success and mutual commitment in addressing Tome's initial challenge.The internal tool significantly enhanced the legal team's efficiency, enabling them to handle discrepancies and improve the AI model's accuracy swiftly. Frequent updates and the ability to rapidly implement feedback ensured the tool evolved in line with the legal team's needs. This synergy not only streamlined Tome's workflow but also allowed the engineering team to focus on refining the machine learning model, thereby enhancing the overall product experience for Tome users.



Next.js (React.js)

Chakra UI

Apollo GraphQL Client




Apollo GraphQL Server