Fileread provides an AI-powered document review software integrated with Relativity for litigation teams. This platform enables users to query large document collections, verify findings, and generate accurate work product like memos and chronologies with direct document citations. The service accelerates case preparation by quickly identifying key insights across text, images, and unstructured data while adhering to stringent security and compliance standards.
Funding
$6.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

Founders
Product
Problem
Legal teams face challenges in efficiently analyzing large volumes of data during litigation, which can slow down fact-finding and increase the time and cost associated with case preparation. The manual nature of data review can also lead to oversights and missed connections, potentially impacting case outcomes.
Solution
This startup provides an AI-powered litigation platform designed to streamline data analysis for legal professionals. The platform automates the process of extracting key insights from large datasets, enabling legal teams to accelerate fact-finding and focus on strategic decision-making. By facilitating data dialogue, the AI tools help legal professionals uncover relevant information more quickly and improve the overall quality of their work product, from initial settlement discussions to trial preparation. The platform aims to empower legal teams to make more informed decisions and achieve better outcomes in their cases.
Target Audience
The primary target audience includes legal teams, attorneys, paralegals, and other legal professionals involved in litigation and requiring efficient data analysis tools.
Features
- AI-powered data analysis for efficient fact-finding
- Automated data dialogue to extract key insights
- Tools for early settlement discussions, deposition preparation, and trial preparation
- Enhanced data visualization for improved understanding