Passwork
Passwork is a self-hosted corporate password manager built for organizations that take security seriously. Designed and headquartered in Barcelona, Spain, Passwork meets GDPR, NIS2, ENS, and other European regulatory standards by default.
Every password and credential lives exclusively on your own server. A double-layer AES-256 encryption model — applied on both the server and client sides — combined with zero-knowledge architecture ensures your data never leaves your infrastructure. System administrators retain full, uninterrupted control.
Passwork holds ISO/IEC 27001 certification. Enterprises rely on it for secure password sharing, privileged access management, and centralized credential governance — all without exposing sensitive data to third-party systems.
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4ALLPORTAL
If you are looking for a way to easily manage your product data, 4ALLPORTAL is the hub for you. Our software saves resources! Increase sales, reduce costs and get more time for strategy and creativity. Maintain your product data once, link information and media with all products and keep them up-to-date in all sales channels with just a few clicks.
Because our platform is highly customizable and scalable, we can create a solution specifically tailored to your needs. Your dedicated account manager will ensure that the software grows with your needs.
Interested? Here's how it works:
Step 1: In a 30-minute call, you tell us about your current and future needs and the problems you face in your daily work.
Step 2: We evaluate your needs and create a customized 4ALLPORTAL, which we present to your team in a live demo.
Step 3: You get access to your 4ALLPORTAL for 30+ days to test it extensively and decide if you want to work with us or not.
What are you waiting for? Start managing your data efficient today and scale your business with 4ALLPORTAL.
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Scale Data Engine
Scale Data Engine empowers machine learning teams to enhance their datasets effectively. By consolidating your data, authenticating it with ground truth, and incorporating model predictions, you can seamlessly address model shortcomings and data quality challenges. Optimize your labeling budget by detecting class imbalances, errors, and edge cases within your dataset using the Scale Data Engine. This platform can lead to substantial improvements in model performance by identifying and resolving failures. Utilize active learning and edge case mining to discover and label high-value data efficiently. By collaborating with machine learning engineers, labelers, and data operations on a single platform, you can curate the most effective datasets. Moreover, the platform allows for easy visualization and exploration of your data, enabling quick identification of edge cases that require labeling. You can monitor your models' performance closely and ensure that you consistently deploy the best version. The rich overlays in our powerful interface provide a comprehensive view of your data, metadata, and aggregate statistics, allowing for insightful analysis. Additionally, Scale Data Engine facilitates visualization of various formats, including images, videos, and lidar scenes, all enhanced with relevant labels, predictions, and metadata for a thorough understanding of your datasets. This makes it an indispensable tool for any data-driven project.
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Dioptra
Select the most impactful unlabeled data to enhance domain coverage and boost model performance. Ensure your metadata is registered with Dioptra while retaining full control over your data. Identify the underlying causes of model failure and regressions through a comprehensive data-focused toolkit. Utilize our active learning miners to extract the most valuable unlabeled datasets. Leverage Dioptra’s APIs to seamlessly integrate with your labeling and retraining processes. Systematically curate your data at scale tailored to your specific use case. We offer open-source solutions for data curation and management applicable to computer vision, NLP, and LLMs. Our support has enabled clients to elevate model accuracy on challenging cases, accelerate training durations, and cut down on labeling expenses, ultimately leading to more efficient workflows. This approach not only streamlines the data management process but also fosters innovation in model development.
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