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Synthium Labs
Synthium Labs

Solutions

Synthium Evaluate

Evaluating difficult model outputs — open-ended answers, reasoning, safety and instruction-following — demands expert human judgment. Generic annotators can tell you what's fluent; they can't reliably tell you what's correct, sound or safe. That gap is what we close.

  1. Model
  2. Tasks
  3. Expert Review
  4. Scoring
  5. Insights

What we evaluate

The outputs where judgment matters

When correctness, reasoning or safety is on the line, the evaluator needs to be as capable as the task.

  • Open-ended answers and explanations
  • Reasoning traces and step-by-step logic
  • Safety boundaries and refusals
  • Tool use and function-calling behavior
  • Instruction-following and constraint adherence

How experts are calibrated

Aligned before they touch production

Contributors are assessed against project-specific rubrics and gold standards, then continuously scored. The quality bar is fixed before a single label ships.

Output formats

Delivered the way your pipeline consumes it

Preference labels for model-vs-model comparisons
Structured critiques with rationale
Severity-rated issue logs
Rubric-based quality scores

Who it's for

AI labs and frontier model companies who need defensible, high-signal human evaluation of model behavior — not a volume play, but a judgment play. If the question is “is this output actually good?”, that's the problem we solve.

Next

See how we hold the line on quality.