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Details

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Aieval Digital Main Features

• Cost Control
  It predicts and manages AI project costs to avoid surprises in bills.
• Intake Templates
  There are 7 templates to help teams start new AI projects easily.
• Score Dimensions
  There are 4 areas to measure AI projects, including AI quality, security, privacy, and recommendations.
• Measuring Success
  It creates clear ways to see what AI projects achieve once in use.
• Workflow Statuses
  There are 9 steps to track the progress of AI projects, such as approved, under review, needs revision, and insufficient.
• Organizational Memory
  It keeps a record of decisions and learnings so the company doesn't repeat mistakes.
• Risks and Improvements
  It identifies potential problems like outdated answers and hallucination, and suggests ways to improve, like regular knowledge updates.
• Security and Privacy Risks
  It helps track sensitive data like PII to prevent security problems when sharing with vendors.
• Evaluation Before Deployment
  Projects are tested thoroughly for risks and costs, not just on demo performance.
• AI Quality and Recommendations
  The system suggests safe and simple AI use cases that are low risk and suitable to test with many users.
• Managing AI across the Company
  It helps organize AI activities happening in different parts of the company to avoid duplicates and security issues.
• Standard Criteria for Projects
  Teams follow common checklists or standards for approving AI projects, making decisions more consistent.
• Vendor and Solution Management
  It prevents teams from hiring vendors directly or creating duplicate solutions without awareness.
• Avoiding Simple Problems with AI
  Encourages using clear rules instead of complex AI for simple tasks to get better results.
• Shared Framework for AI Initiatives
  It provides a common way to handle the start, review, and management of AI projects, helping teams work effectively without delays.

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