AI evaluation and benchmarking

An independent evaluation company for foundation models and agents

Latent Space — The AI Frontier: from open weights to open research — Eiso Kant, Poolside AI

Create standardized evaluations that measure models and agent systems consistently, prevent benchmark cheating, and allow competing foundation-model companies to be compared under the same execution conditions.

Problem: Self-reported benchmarks can be difficult to compare because companies may run evaluations differently, use different infrastructure or tools, or inadvertently optimize for the benchmark rather than real-world capability.

For: Foundation-model companies, model users, investors, and developers who need trustworthy comparisons of models and agent systems.

Examples

• Artificial Analysis: Poolside identified it as a third party that could run independent evaluations of models, including checking the relevant infrastructure and tools.