Opper helps bring more structure and consistency to our AI-driven compliance checks. The ability to trace and review each task is useful when we need to understand or explain how a result was produced. Overall, it’s a practical framework that has reduced a fair bit of manual effort in our validation processes. It’s also been straightforward to integrate into our existing stack, and the interface is easy for our team to work with day-to-day. We use it regularly across our validation flows. Their customer support has been amazing, whenever we’ve needed clarification or guidance, and the broad model selection — including the option to use our own — has been helpful for our use cases.

The datasets, evals, and tracing work really well together to maintain quality at scale. We can see exactly what's happening with each result and continuously improve using real examples from our experts. The fallback models solved our rate limit issues, and the transparent API means we own our logic and can export everything if needed. We're not locked into a single model provider.

The multi-model flexibility is huge for us. We're not locked into a single provider, we can swap models based on what works best for each task. The JSON schemas and evaluation scores give us confidence when switching between models, and the fallback options keep everything reliable. The tracing has been critical for tuning our user experience and hiding latency in real-time.