Put Your AI Interpreting to the Test: How to Evaluate Test Calls (Infographic)

Lab testing is only the beginning when evaluating AI interpreting. True confidence comes from seeing how AI performs with real people, in real environments, under your own real-world conditions. 

Our new infographic, AI Interpretation: Evaluating Test Calls, breaks down the key areas every organization should consider when putting AI interpreting to the test. These factors reveal not just whether AI "works," but whether it works for you in the settings and scenarios that matter most.

What to Look For in Test Calls

• Environment Fit: Does the AI hold up in your actual operational setting, with your users?
• Noise & Audio Quality: Can it filter out background chatter, handle poor microphones, or manage distant voices?
• Clarity & Complexity: How well does it process different accents, dialects, or complex sentence structures?
• Speaker Overlap: Can it separate voices when multiple people talk at once?
• Industry Terminology: Is it trained to interpret your specialized terms and colloquialisms accurately?
• Risk Management: What happens when an error occurs? Does the system recognize it and escalate appropriately?

These questions matter because they determine whether AI interpretation delivers reliability and safety where it counts. Structured test calls help you identify where AI is ready to add value and where human interpreters remain essential.

Download the Infographic

Get the full breakdown and start building your own evaluation framework today. Download the infographic: Evaluating Test Calls