Jscrambler is the leader in Client-Side Protection and Compliance. We were the first to merge advanced polymorphic JavaScript obfuscation with fine-grained third-party tag protection in a unified Client-Side Protection and Compliance Platform.
Our end-to-end solution does more than protect your data—it empowers your business. With Jscrambler, your teams are free to take full advantage of client-side JavaScript innovation, assured that your business benefits from blanket protection against current and emerging cyber threats, data leaks, misconfigurations, and IP theft. Jscrambler is the only solution that enables the definition and enforcement of a single, future-proof security policy for client-side protection. We also make it easy to comply with new standards and regulations; our dedicated PCI module helps businesses meet the stringent requirements of PCI DSS v4 (6.4.3 and 11.6.1).
Trusted by digital leaders worldwide, Jscrambler lets you move fast and embrace a culture of fearless innovation while ensuring that both your first- and third-party client-side JavaScript assets remain secure and compliant.
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Reliable and transparent time recording is vital for flexible working models, hybrid teams, and complex collective agreements or legal requirements. ZEUS® Time and Attendance from ISGUS is a smart digital solution that seamlessly integrates into your business processes, providing employees and managers with maximum transparency, flexibility, and efficiency.
ZEUS® Time and Attendance enables your employees to record working hours, breaks, shifts, and home office hours legally, flexibly, and regardless of location—via terminal, web browser, or mobile app. The data is processed in real time and is immediately available for evaluation, approval, and further use. ZEUS® Time and Attendance covers all legal, collective, and company regulations, including rest periods, overtime, and core working hours.
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LayerLens
LayerLens serves as an autonomous platform dedicated to evaluating AI models, providing insights into their performance through verified benchmarks, prompt-specific outcomes, agentic comparisons, and audit-ready assessments across different vendors. This platform enables teams to conduct side-by-side comparisons of over 200 AI models, utilizing transparent benchmarks and consistent evaluation techniques focused on accuracy, latency, behavior, and practical application in real-world scenarios. Designed for comprehensive model analysis, LayerLens features Spaces that allow teams to organize benchmarks and evaluations, identify strengths in tasks, and monitor performance trends in relevant contexts. The platform also facilitates ongoing evaluations by continuously assessing model updates, prompt modifications, judge changes, and live traces, thereby empowering teams to identify issues like quality regressions, drift, silent failures, contamination, and policy concerns before they impact production. By prioritizing transparency and collaboration, LayerLens ensures that teams can make informed decisions about their AI model choices.
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LLM Scout
LLM Scout serves as a thorough platform for evaluation and analysis, assisting users in benchmarking, comparing, and interpreting the capabilities of large language models across various tasks, datasets, and real-world prompts, all within a cohesive environment. By allowing side-by-side comparisons, it assesses models based on accuracy, reasoning, factuality, bias, safety, and other vital metrics through customizable evaluation suites, curated benchmarks, and specialized tests. Users can integrate their own data and queries to evaluate how different models perform in relation to their specific workflows or industry requirements, with results visualized in an intuitive dashboard that underscores performance trends, strengths, and weaknesses. Additionally, LLM Scout offers functionalities for examining token usage, latency, cost effects, and model behavior under different scenarios, thereby equipping stakeholders with the insights needed to make educated choices regarding which models align best with particular applications or quality standards. This comprehensive approach not only enhances decision-making but also fosters a deeper understanding of model dynamics in practical contexts.
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