Long Context vs RAG: Testing Which Approach Finds the Right Evidence
A controlled benchmark compares long-context models and retrieval pipelines on recall, citation accuracy, latency, and cost across realistic document-heavy tasks.
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A controlled benchmark compares long-context models and retrieval pipelines on recall, citation accuracy, latency, and cost across realistic document-heavy tasks.
We test compact language models on factory, retail, and field-service hardware to measure offline accuracy, memory demands, power use, and operational resilience.
A benchmark probes whether multimodal models can interpret axes, legends, anomalies, and uncertainty across business charts rather than exploit superficial cues.