
Scaleout Systems
Decentralized AI & Federated learning software platform to develop privacy-preserving solutions for computer vision, natural language processing, anomaly detection and more.
Date | Investors | Amount | Round |
---|---|---|---|
- | investor | €0.0 | round |
investor | €0.0 | round | |
investor | €0.0 | round | |
investor investor investor investor investor | €0.0 | round | |
investor investor | €0.0 | round | |
* | SEK35.0m | Early VC | |
Total Funding | 000k |
EUR | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 |
---|---|---|---|---|---|---|
Revenues | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 |
% growth | - | 22 % | (17 %) | 39 % | 19 % | 10 % |
EBITDA | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 |
% EBITDA margin | - | - | - | 18 % | 5 % | - |
Profit | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 |
% profit margin | 29 % | (10 %) | 8 % | 3 % | (9 %) | - |
EV | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 |
EV / revenue | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x |
EV / EBITDA | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x |
R&D budget | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 |
Source: Company filings or news article, Dealroom estimates
Related Content
Scaleout Systems specializes in decentralized AI and federated learning, offering solutions that allow for the training and distribution of machine learning models across a vast network of devices. The company operates in the AI and machine learning market, focusing on clients who require scalable and resilient federated learning frameworks. By leveraging technologies like Tensorflow, Torch, and sklearn, Scaleout Systems enables clients to integrate federated learning capabilities into their MLOps workflows. The business model revolves around providing a platform that supports computer vision, natural language processing, and fraud detection, among other applications. Scaleout Systems generates revenue by offering its FEDn framework, which is designed to meet the growing demand for privacy-focused AI solutions that comply with regulatory requirements. The company addresses the need for data processing at the edge, where data is created, thus aligning with global macrotrends that emphasize data privacy and integrity.
Keywords: federated learning, decentralized AI, model distribution, intelligent edge, privacy-focused, MLOps integration, computer vision, NLP, fraud detection, regulatory compliance.