Scorecard

Product Introduction

1. Scorecard is a platform designed for teams developing AI agents in high-stakes industries, such as healthcare, finance, and legal sectors, where reliability and accuracy are critical. It integrates LLM evaluations, human feedback, and product telemetry to automate agent learning and improvement cycles. The platform enables continuous testing, optimization, and deployment of AI systems with measurable benchmarks.
2. The core value of Scorecard lies in its ability to unify development, testing, and production environments into a single feedback loop, reducing risks and accelerating iteration cycles. It provides teams with actionable insights to identify performance gaps, validate improvements, and deploy AI agents confidently in real-world scenarios.

Main Features

1. Live Observability: Scorecard offers real-time monitoring of AI agent interactions, enabling teams to track user engagement, system failures, and unexpected behaviors as they occur. This feature uses continuous evaluation to flag issues like hallucination risks, compliance violations, or logic errors, allowing immediate intervention. Metrics are visualized through dashboards that correlate agent performance with business outcomes.
2. Versioned Prompt Management: Teams can create, test, and track multiple versions of prompts or agent configurations in a centralized repository. This feature supports A/B testing and experiment tracking, ensuring that only high-performing iterations progress to production. Historical data is retained to audit changes and replicate successful outcomes across environments.
3. Validated Metric Library: Scorecard provides pre-built evaluation metrics aligned with industry standards, such as accuracy, safety, and regulatory compliance, which teams can customize or extend. These metrics are applied automatically during testing phases to generate performance reports, benchmark agents against peers, and validate improvements before deployment.

Problems Solved

1. Slow Feedback Cycles: Traditional AI development workflows require manual testing and delayed user feedback, which prolongs improvement cycles. Scorecard automates evaluation across development and production, providing immediate insights to accelerate iterations.
2. Enterprise AI Teams: The platform targets technical and non-technical stakeholders in regulated industries who need to ensure AI agents meet strict performance, safety, and compliance standards. Users include ML engineers, product managers, and compliance officers.
3. High-Risk Deployment Scenarios: Typical use cases include validating AI-driven medical diagnosis tools, auditing financial advisory agents for regulatory adherence, and stress-testing customer service bots to prevent harmful outputs before release.

Unique Advantages

1. Integrated AI Control Room: Unlike siloed tools that separate testing from production monitoring, Scorecard connects all stages of the AI lifecycle. This integration allows teams to analyze how code changes impact real-world performance using live user data.
2. Structured Experimentation Framework: The platform’s Playground environment enables rapid hypothesis testing, where teams can simulate edge cases, modify prompts, and measure outcomes against predefined metrics without writing code.
3. Enterprise-Grade Governance: Scorecard outperforms competitors by offering granular access controls, audit trails, and compliance-ready reporting features. Its continuous evaluation system detects emerging risks in production, such as data drift or policy violations, before they escalate.

Frequently Asked Questions (FAQ)

1. How does Scorecard differ from traditional testing frameworks? Scorecard combines automated LLM evaluations with real-user feedback and product telemetry, creating a closed-loop system that tests agents in both controlled and live environments. Traditional tools lack integration with production data or human-in-the-loop validation.
2. Can Scorecard integrate with existing ML pipelines? Yes, the platform supports APIs and pre-built connectors for popular MLops tools, cloud providers, and LLM platforms like OpenAI or Anthropic. Teams can import existing datasets, prompts, and agent configurations without disrupting workflows.
3. How does real-time observability handle large-scale deployments? Scorecard uses distributed tracing and sampling to monitor thousands of concurrent agent interactions without latency. Critical issues trigger alerts via Slack, email, or PagerDuty, while non-critical data is stored for batch analysis.
4. Are custom evaluation metrics supported? Teams can define custom metrics using Python or a no-code editor, incorporating domain-specific rules, third-party APIs, or proprietary algorithms. These metrics are validated against historical data to ensure consistency.
5. What deployment options are available? Scorecard supports cloud-hosted and on-premises deployments, with encryption for data at rest and in transit. Agents can be deployed directly from the platform to Kubernetes clusters, serverless environments, or API endpoints.