Our proven automated technology automates data curation and empowers life sciences experts with AI-ready data to rapidly and consistently generate breakthroughs.
Netrias’ data-driven, in-silico platform bridges the gap between wet lab experimentation and bench intuition using machine learning and artificial intelligence optimized with expert knowledge. This robust combination enables real-world scientific insight to guide predictive modeling results and accelerate the scientific process.
AI-Driven Data Harmonization
Our automated technology processes raw, unformatted assay data into AI-ready dataframes by isolating data from assay file formats, combining like and similar data, and correcting names, typos, unifying headers and other applicable information. Completed quickly and with minimal human curation, this harmonized data is ready for model analysis.
An expert team of data scientists, microbiologists and software engineers, we utilize deployable, automated workflows and predictive modeling to streamline the entire data curation process from end-to-end, and determine the next steps in scientific validation and exploration.
Our agility sets us apart.
Device & Data Agnostic
Curating varying data from disparate systems, assays, file formats, and bioinformatics tools is the cornerstone of our solutions.
Diverse Client & Project Portfolio
With clients across academia, government, and industry, our project and subject matter experience is both complex and comprehensive.
Multifaceted Team & Experience
Our cohesive team of data scientists, microbiologists, and software engineers have diverse experience and backgrounds that give us depth and perspective.
Established Reputation & Agile Operations
Prominent in the life sciences sector, our team is known to be both standard-setting and capable of pivoting swiftly when required by an ever-changing field.
Research Contract Capable
In our efforts to make an industry impact, we excel at incubating novel technology and transitioning it to larger government and commercial agencies.