Observability & Evaluations
Track AI performance in production. Automatic traces, custom metrics, quality scoring, and safety monitoring for every call.
Trusted by leading companies
Challenge
You can't improve what you can't measure, and you can't debug what you can't see.
AI models are probabilistic. The same input can produce different outputs, making consistent quality difficult without testing and monitoring.
When AI fails, you can't see why. Bad prompt? Hallucination? Data issue? Without observability, debugging is guesswork.
No monitoring for inappropriate content, PII leaks, or policy violations means significant business risk. Post-production discovery is too late.
Manual review doesn't scale. Automated evaluations are needed, but building evaluation infrastructure takes months.
The Opper Way
Automatic quality scoring, custom metrics, and dataset evaluations. Track what matters for your AI applications.
Every task completion gets a quality observation within 1-10 seconds. View summary and 0-100 score in the tracing UI.
Attach custom metrics to task completions or spans. Measure conciseness, relevance, accuracy—any dimension you choose. All metrics tracked and visible in traces.
Run evaluations against task datasets to test new models, prompts, or configurations. Compare results side-by-side before production changes.
Structured, declarative tasks are easier to evaluate than raw LLM completions. Define clear schemas and requirements for consistent evaluations.
class Score(BaseModel):
thoughts: str = Field(
description="Thoughts on how to evaluate the response",
)
observations: str = Field(
description="Observations about the operation and the response",
)
correct: bool = Field(
description="Did the model succeed at handling the task or not?",
)
score: float = Field(
description="A value between 0 and 100 reflecting the quality of the response given the instructions, input and expected output",
)Better schema annotations lead to clearer tasks and more accurate evaluations.
Automatic safety and policy monitoring. Track blocked prompts, content violations, and safety interventions in production.
Case Study
AI-BOB uses Opper's Task Completion API to transform plain-language building requirements into reliable, auditable compliance checks — with schema-enforced outputs, built-in evaluators, and full observability embedded directly in architects' workflows.