The problem every LLM team faces
You've shipped your LLM feature. It looked great in testing. Now it's in production, handling real queries, and you have no structured way to know if it's still working.
Maybe answer quality has drifted since the last prompt change. Maybe a specific query type is hallucinating at a higher rate than before. Maybe a new document type was added to your RAG corpus and retrieval silently degraded. You won't find out from your users until it's too late — or from your team until someone manually spot-checks a sample.
This is the gap BEval Studio closes.
What it is
BEval Studio is a hosted evaluation platform for production LLM systems. It gives you structured, continuous visibility into how your model is performing — not on a benchmark, but on your actual production traffic.
The integration is a single Python library:
pip install beval
You instrument your inference pipeline once — wrapping your LLM calls, your retrieval steps, or your agent runs — and BEval captures the inputs, outputs, and intermediate steps. That's it. No infrastructure to provision. No database to manage. The logs flow into a consolidated dashboard that we maintain.
From there, we run evaluations.
How evaluation works
The eval runs happen on our side, against your logged production traffic. We have a library of research-backed evaluation methods covering the failure modes that matter most in production:
- Faithfulness — does the answer stay grounded in the retrieved context, or is the model adding information that isn't there?
- Answer relevance — does the output actually 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 does it match the expected register?
For teams that have done their own evaluation research — custom rubrics, domain-specific quality criteria, annotated test sets — we can run those too. BEval Studio is the infrastructure; the evaluation logic is configurable.
Results land in your dashboard as time-series scores. You see quality over time, broken down by query type, model version, or whatever dimensions matter to your system.
What you get in the dashboard
The dashboard is the single place where you can answer: is my LLM still working the way it should?
You get:
- Trend lines per eval metric over time — so you see when something started degrading, not just that it has
- Run-level drill-down — click into any eval run to see the specific input, output, retrieved context, and score breakdown
- Failure tagging — outputs that score below threshold are surfaced automatically, grouped by failure type
- Prompt version comparison — when you change a prompt, you see the before/after in eval scores, not just in vibes
How it compares to the alternatives
LangSmith, DeepEval, Ragas — these are all real tools solving a real problem, and BEval Studio is solving the same problem. The difference is in where the evaluation expertise sits.
Most platforms give you the scaffolding and ask your team to define the evaluation logic. That works if you have an ML researcher who knows how to design robust evals. Most teams don't — and even those that do often don't have time to maintain them as the system evolves.
With BEval Studio, the evaluation research is on us. We maintain the eval library, we track the literature, and we run the evals. Your team instruments once and reads the results.
For teams who do have their own evaluation research, BEval runs that too. It's not either/or.
Who it's for
BEval Studio is built for teams who have shipped an LLM system and need to maintain quality in production — not for teams still building.
If you're in a demo phase, you don't need this yet. If you're in production, you probably already feel the gap it closes.
Current users include legal AI teams (monitoring citation accuracy and hallucination rates), enterprise RAG deployments (tracking retrieval quality across document types), and voice AI systems (measuring dialogue quality on real call flows).
Pricing
BEval Studio starts at $500 / month. That covers hosted evaluation infrastructure, the Python library, dashboard access, and our maintained eval library running against your production logs.
For teams with custom evaluation requirements — domain-specific rubrics, human-in-the-loop review workflows, or high-volume traffic — we scope those separately.
If you're shipping LLMs and flying blind on quality, book a call. We'll show you what BEval looks like against your system.