BEval Studio is live. This post covers what it does, how the evaluation works, what you see in the dashboard, who it's for and what it costs.
The problem
You shipped an LLM feature. It looked great in testing. Now it handles real queries in production, and you have no structured way to know if it still works.
Answer quality may have drifted since the last prompt change. One query type may be hallucinating more than before. A new document type in your RAG corpus may have quietly broken retrieval.
You usually find out from users, or when someone on the team spot-checks a sample by hand. BEval Studio closes that gap.
What it is
BEval Studio is a hosted evaluation platform for production LLM systems. It tracks how your model performs on your actual production traffic, not on a benchmark.
The integration is one Python library:
pip install bolder-ai
You instrument your inference pipeline once by wrapping your LLM calls, retrieval steps or agent runs. BEval captures the inputs, outputs and intermediate steps.
There's no infrastructure to provision and no database to manage. Logs flow into a dashboard we maintain, and we run evaluations from there.
How evaluation works
Evals run on our side, against your logged traffic. Our library of research-backed methods covers the failure modes that show up most in production:
| Eval | What it checks |
|---|---|
| Faithfulness | Does the answer stay grounded in the retrieved context? |
| Answer relevance | Does the output address what was asked? |
| Context recall | Did retrieval surface what it needed to? |
| Hallucination detection | Is the model asserting facts it can't support? |
| Coherence and tone | Does the response hold together and match the expected register? |
If your team has its own evaluation research (custom rubrics, domain-specific criteria, annotated test sets), we run that too. BEval Studio is the infrastructure. The evaluation logic is configurable.
Results land in your dashboard as time-series scores, broken down by query type, model version or whatever dimensions matter to your system.
What's in the dashboard
The dashboard answers one question: is my LLM still working the way it should?
- Trend lines per eval metric, so you see when something started degrading, not just that it did.
- Run-level drill-down into any eval run: the input, output, retrieved context and score breakdown.
- Failure tagging. Outputs below threshold are surfaced automatically and grouped by failure type.
- Prompt version comparison. Change a prompt and see the before and after in eval scores.
How it compares
LangSmith, DeepEval and Ragas are real tools solving the same problem. The difference is who owns the evaluation expertise.
Most platforms give you the scaffolding and ask your team to define the eval logic. That works if you have an ML researcher who knows how to design solid evals. Most teams don't, and the ones that do rarely have time to keep those evals current as the system changes.
With BEval Studio, the evaluation research is on us. We maintain the eval library, track the literature and run the evals. Your team instruments once and reads the results.
If you do have your own evaluation research, BEval runs that as well.
Who it's for
BEval Studio is for teams that have shipped an LLM system and need to keep quality up in production. If you're still in a demo phase, you don't need it yet.
Current users include:
- Legal AI teams monitoring citation accuracy and hallucination rates
- Enterprise RAG deployments tracking retrieval quality across document types
- Voice AI systems measuring dialogue quality on real call flows
Pricing
BEval Studio starts at $500 / month. That covers the hosted evaluation infrastructure, the Python library, dashboard access and our maintained eval library running against your production logs.
Custom requirements such as domain-specific rubrics, human-in-the-loop review workflows or high-volume traffic are scoped separately.
If you're shipping LLMs without a clear view of quality, book a call and we'll show you BEval running against your system.