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What is Asset Track Cloud?

Antares Vision Group's Asset Track Cloud platform represents a cutting-edge, cloud-based solution specifically designed to enhance logistics, offering improved visibility, reduced waste, and supporting sustainable practices. This innovative platform enables businesses within the cosmetics industry to carefully track their returnable assets such as pallets, cartons, and primary packaging, which in turn leads to lower operational costs and improved Environmental, Social, and Governance (ESG) ratings. By leveraging unique digital identities, RFID technology, and visual recognition, this solution guarantees full traceability, seamlessly integrating data collection tools with ERP systems to provide a comprehensive overview of asset conditions. Additionally, the sophisticated analytics framework empowers organizations to significantly minimize losses while optimizing their performance, ultimately rendering their supply chains more effective and environmentally conscious. The implementation of an integrated tracking system that combines RFID tags, barcodes, and solutions like the Asset Track Cloud from Antares Vision Group is essential for contemporary businesses. In a marketplace that is continuously evolving, adopting such advanced technological solutions allows companies to maintain a competitive advantage and adapt to changing consumer demands. Moreover, the long-term benefits of sustainability and efficiency gained from this platform foster a more responsible approach to business operations.

What is Antares?

Antares is a collection of open-weight security small language models crafted to detect vulnerabilities within large codebases. Featuring models such as Antares-350M and Antares-1B, these tools can be deployed locally or on-site, ensuring that proprietary source code remains secure while also reducing both inference expenses and runtime. The procedure starts with an outline of the vulnerability, which may include an advisory or a CWE category; from there, the model embarks on a detailed investigation similar to that of a human analyst, methodically looking for relevant code patterns, scrutinizing possible files, integrating new data, and adjusting its strategy when certain paths appear unproductive. This method allows the model to concentrate its resources on the files most likely to contain the identified flaws. In the end, Antares produces a prioritized list of source files that may be vulnerable, accompanied by a comprehensive trail of the exploration process that led to these conclusions, thereby simplifying the review and prioritization for teams. Furthermore, this functionality not only accelerates the vulnerability assessment process but also significantly strengthens the overall security framework of the development environment, fostering a culture of proactive security measures. Ultimately, organizations can benefit from improved efficiency and effectiveness in managing their code vulnerabilities.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Antares Vision Group

Company Location

Italy

Company Website

antaresvisiongroup.com/cosmetics/supply-chain-transparency/returnable-assets/

Company Facts

Organization Name

Cisco

Date Founded

1984

Company Location

United States

Company Website

blogs.cisco.com/ai/introducing-antares-the-most-efficient-open-weight-ai-models-for-vulnerability-localization

Categories and Features

Asset Tracking

Not specified

Categories and Features

AI Models

Not specified

AI Security

Not specified

Open-Weight AI Models

Not specified

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