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Scorecard

💲Paid

Scorecard is a platform for building, testing, evaluating, and optimizing enterprise AI agents and LLM apps. It provides tools for continuous evaluation, performance testing, and prompt management to ensure reliable AI performance in production. The platform helps teams catch issues early, fix them quickly, and improve AI agents with each update.

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Platform
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AI EvaluationLLM TestingAI OptimizationMLOpsAI PerformanceContinuous EvaluationPrompt Management

What is Scorecard?

Scorecard is a platform designed to assist teams in developing, testing, evaluating, optimizing, and deploying enterprise AI agents, with a focus on LLM applications. It provides tools for continuous evaluation, performance testing, and prompt management to ensure predictable and reliable AI experiences that improve over time. The platform helps users monitor AI model behavior, identify issues early, resolve them quickly, and maintain stability in production environments. By addressing challenges like slow feedback cycles and silos between development and production, Scorecard creates a seamless feedback loop for AI development.

Core Technologies

  • Natural Language Processing
  • Machine Learning
  • Cloud Computing

Key Capabilities

  • Continuous evaluation of AI model performance
  • Performance testing for LLM applications
  • Prompt management for optimized outputs
  • Early detection and resolution of AI issues
  • Reliable deployment of AI agents in production
  • Seamless feedback loop between development and production

Use Cases

  • Enterprise AI agent development
  • LLM app optimization
  • Performance benchmarking for AI models
  • Continuous improvement of production AI systems

Core Benefits

  • Predictable and reliable AI experiences
  • Early detection of performance issues
  • Faster iteration and optimization cycles
  • Improved AI agent performance over time
  • Seamless transition from development to production

Key Features

  • Continuous evaluation of AI agents
  • Performance testing with vetted metrics
  • Prompt management tools
  • Real-world performance monitoring
  • AI lab for creating experiments
  • Feedback loop for iterative improvements

How to Use

  1. 1
    Sign up for Scorecard and connect your AI agent to the platform
  2. 2
    Create experiments in the AI lab to test agent performance with vetted metrics
  3. 3
    Analyze test results and optimize your agent based on feedback
  4. 4
    Deploy the improved agent to production and monitor real-world performance
  5. 5
    Iterate using continuous feedback from production to refine your agent

Pricing Plans

Starter

$0/Month
Essential evaluations for early-stage AI projects. Unlimited users, 100,000 scores.

Growth

$299/Month
Reliable AI evaluations for startups and mid-sized companies. Unlimited users, includes 1M scores/mo, then $1 per 5K. Test set management, prompt playground access, priority support.

Enterprise

Customized Pricing
Custom solutions for large-scale AI deployments. Everything in Growth, SAML single sign-on (SSO) & authentication management, SOC 2 compliance reporting, end-to-end data encryption (including at rest), 24/7 VIP support, volume-based usage discounts, customizable contract terms.

Frequently Asked Questions

Q.What problem does Scorecard solve in AI development?

A.Scorecard addresses the problems of slow feedback cycles and silos between development and production, which hinder innovation and understanding of AI performance.

Q.How does Scorecard help ensure predictable AI experiences?

A.Scorecard provides continuous evaluation of AI behavior, allowing teams to catch problems early, fix them fast, and ship AI agents that work reliably.

Q.Can I customize the metrics used to evaluate my AI agents with Scorecard?

A.Yes, Scorecard offers a validated metric library with industry benchmarks, and you can customize proven metrics or create your own to track what matters most to your business.

Q.Does Scorecard support managing and versioning prompts?

A.Yes, Scorecard allows you to create, test, and track your best-performing prompts all in one place, maintaining a history and providing a single source of truth for your team.

Pros & Cons (Reserved)

✓ Pros

  • Enables continuous evaluation for reliable AI performance
  • Provides real-world monitoring to catch issues early
  • Facilitates faster iteration with prompt management tools
  • Improves AI agent performance through feedback loops
  • Seamlessly transitions from development to production environments

✗ Cons

  • May have a steep learning curve for beginners
  • Pricing could be prohibitive for small teams
  • Limited integration options with certain AI frameworks

Alternatives

No alternatives provided.