FlowView Diagnostics
FlowView Diagnostics offers an AI-powered flow cytometry data analysis software designed to automate cell analysis for labs, hospitals, and research teams. Their primary product, the Asudes platform, utilizes a patented AI algorithm called Eclipse to process complex, high-dimensional datasets, turning them into intuitive 2D visuals for faster and more accurate decision-making. The software addresses challenges like manual gating, interpretation errors, and scaling large datasets by speeding up detection from hours to minutes, providing automated insights understandable by any clinician, and ensuring reproducibility through a deterministic AI approach. This leads to improved research outcomes and better patient care by delivering timely, reliable clinical insights.
FlowView Diagnostics
FlowView Diagnostics offers an AI-powered flow cytometry data analysis software designed to automate cell analysis for labs, hospitals, and research teams. Their primary product, the Asudes platform, utilizes a patented AI algorithm called Eclipse to process complex, high-dimensional datasets, turning them into intuitive 2D visuals for faster and more accurate decision-making. The software addresses challenges like manual gating, interpretation errors, and scaling large datasets by speeding up detection from hours to minutes, providing automated insights understandable by any clinician, and ensuring reproducibility through a deterministic AI approach. This leads to improved research outcomes and better patient care by delivering timely, reliable clinical insights.
TL;DR
Product: Asudes — AI SaaS for automated flow cytometry data analysis using the ECLIPSE algorithm
Founded: 2020 (Netherlands)
Focus: Clinical and research flow cytometry, MRD/rare-event detection
Recent signal: Participant in EU RESOLVE consortium (May 2024)
Reported funding: €875,000 raised from a consortium including NOM and Steppingstone Fund
Company Overview
Problem Domain
Automating and standardizing flow cytometry data analysis to reduce manual gating, interpretation errors, and to enable scalable, reproducible detection of rare events (e.g., MRD) for clinical and research workflows.
Founded
2020
Industry
DeepTech
Tech Stack
Funding Track Record
2850119
Amount recorded in source with USD currency
€875,000
Raised from a consortium including NOM and the Steppingstone Fund (reported by company)
Investor Signal
“Investors reported include Amatis, NLC, NOM, and the Steppingstone Fund; company participation in EU RESOLVE consortium (May 2024)”