Public Sector Test & Evaluation
Test and evaluate AI for safety, performance, and reliability.
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Test Diverse AI Techniques
Public Sector Test & Evaluation for Computer Vision and Large Language Models.
Computer Vision
Measures model performance and identify model vulnerabilities.
Generative AI
Minimize safety risks through evaluating model skills and knowledge.
Why Test & Evaluate AI
Protect the rights and lives of the public. Ensure AI can be trusted for critical missions and workflows.
Rollout AI with Certainty
Have confidence that AI is trustworthy, safe, and meets benchmarks
Ongoing Evaluation
Continuously evaluate your AI models for safe updates and perpetual use
Uncover model vulnerabilities
Simulate real-world context to mitigate unwanted bias, hallucinations, and exploits
Test & Evaluation Methodology
Trusted by governments and leading commercial organizations.
Advanced Retrieval Augmented Generation (RAG) Tools
Holistic evaluation that assesses AI capabilities and determines levels of AI safety
Leverage human experts and automated benchmarks to scalably and accurately evaluate models
Flexible evaluation framework to adapt to changes in regulation, use-cases, and model updates
Test & Evaluate AI Systems with Scale Evaluation
Scale Evaluation is a platform encompassing the entire test & evaluation process, enabling real-time insights on performance and risks to ensure AI systems are safe.
Bespoke GenAI Evaluation Sets
Unique, high-quality evaluation sets across domains and capabilities ensure accurate model assessments without overfitting.
Targeted Evaluations
Custom evaluation sets focus on specific model concerns, enabling precise improvements via new training data.
Rater Quality
Expert human raters provide reliable evaluations, backed by transparent metrics and quality assurance mechanisms.
Product Experience
User-friendly interface for analyzing and reporting on model performance across domains, capabilities, and versioning.
Reporting Consistency
Enables standardized model evaluations for true apples-to-apples comparisons across models.
Red-teaming Platform
Prevent generative AI risk or algorithmic discrimination by simulating adversarial prompts and exploits.