The startup offers a data management platform that automates the maintenance of connections between critical information and manages meta-information specific to materials science. This technology reduces the time researchers spend on complex data handling tasks, minimizing oversights and the need for rework.
Funding
$630K 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
Research and development labs often struggle with managing and connecting diverse data types generated from various instruments and experiments. Critical information is often stored in disconnected spreadsheets and files, hindering collaboration, data accessibility, and efficient analysis. This fragmented data landscape leads to wasted time, increased errors, and difficulty in leveraging data for informed decision-making.
Solution
Randeft offers a data management platform designed to streamline research and development workflows by connecting experimental data with relevant sample information. The platform provides a centralized environment for managing, sharing, and visualizing data from diverse sources, eliminating the need for disparate spreadsheets and file systems. By linking data to its context, Randeft enables researchers to easily find, understand, and utilize information, fostering collaboration and accelerating discovery. The system promotes data-driven decision-making by ensuring data integrity, improving data accessibility, and facilitating comprehensive analysis.
Target Audience
Randeft is designed for researchers, scientists, and engineers in materials science, chemistry, and related fields working in universities, research institutions, and corporate R&D departments.
Features
- Centralized data repository for managing diverse research data types and formats
- Sample tracking and information management to link experimental results to specific materials
- Secure, role-based access control to protect sensitive research data
- Data visualization tools for exploring and analyzing experimental results
- Integration with existing laboratory instruments and data sources
- Audit trails to track data provenance and ensure data integrity
- API for custom integrations and data exchange with other systems
- Compliant with ISO27001:2022 standards