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Simulation Testing

Spectral tests AI systems through Simulation Testing: instead of small, manually curated test sets, it runs complete multi-turn conversations where synthetic users actively pursue goals against your system.

What Test Sets Miss​

Manually curated test sets are small, expensive to build, and inevitably incomplete. You cannot hand-write your way to coverage of every user intent, phrasing variation, or adversarial angle your system will encounter in production.

More critically, they miss the failures that emerge from conversation dynamics. A system can pass every test in your suite and still:

  • Comply with a principle on the first turn, then violate it after a few turns of social pressure.
  • Answer a question correctly in isolation, then contradict itself when the user follows up.
  • Handle in-scope requests correctly, then engage with restricted ones when the framing shifts.

These are not edge cases. They are the normal failure mode of AI systems under real-world conditions.

How Simulation Testing Works​

Spectral solves this by replacing hand-written test cases with synthetic users it builds from a real understanding of your system.

It reads your product's website and documentation to learn what your system does, who its users are, what they come to accomplish, and what rules it must respect.

When you trigger a run, Spectral draws on that understanding to generate hundreds of realistic, goal-driven users. They engage your system in parallel, turn by turn, each one adapting in real time as the conversation unfolds. Every conversation is recorded in full. When it ends, Spectral examines the outcome: did the user achieve their goal, and did the system stay within its defined boundaries?

The results aggregate into compliance rates, aggregate scores, and the failure patterns worth prioritizing.