LLM applications
Compare generated answers, extraction, and classification against task-specific criteria. Check meaning as well as format.
AI & LLM evaluation services
A quality question.
An answer you can inspect.
We define what good looks like for your AI application, test representative cases, and investigate the failures. Your team gets the evaluation artifacts and a clear next decision.
01What we evaluate
Test the system your users experience.
RAG systems
Separate missing evidence, irrelevant retrieval, unsupported claims, and outdated sources. Evaluate the answer and the evidence behind it.
Tool-using agents
Inspect task completion, tool arguments, state changes, retries, and behavior across a conversation.
02The method
Define. Measure. Investigate. Repeat.
A useful evaluation starts with the decision your team needs to make.
Define the quality bar
Agree the task, representative examples, scoring rubric, and failures that matter. Make ambiguous requirements visible before scoring.
Establish the baseline
Run a repeatable evaluation with recorded configuration, outputs, and grading decisions. Keep important failure slices visible.
Investigate and hand over
Connect findings to a testable next change. Deliver the artifacts, readout, and instructions your team needs to continue.
03A focused starting point
One use case.
Ten business days.
The AI Reliability Sprint includes a baseline, failure analysis, one targeted improvement experiment, and a documented handoff. Scope and quote are agreed before work begins.
See the Sprint deliverables04Evidence & useful reading
See how the method works.
Our internal rubric-generator case study documents six representative profiles, three critic lenses, and seven prompt fixes. It demonstrates the method; it does not claim an external client result or measured accuracy uplift.
Read the internal case study05Before we start
A few useful answers.
Can you use our existing evaluation platform?
Yes, where it fits the agreed work. Examples, scoring criteria, calibration, and failure investigation remain important even when a platform is already in place.
Can you run an existing rubric?
We can scope managed evaluation against an existing rubric. Volume, review requirements, cadence, and pricing are agreed separately.
Does evaluation guarantee production readiness?
No. It provides evidence for a bounded decision. A full implementation, ongoing monitoring, or comprehensive security assessment requires separate scope.
07Let’s talk
What isn’t working
the way it should?
Tell us what you’re building, where it breaks, and what you need next. We’ll reply by email to discuss fit and scope.
evals@rubrex.aiWHAT HAPPENS NEXT
- A short email exchange about the problem.
- A technical conversation if there’s a fit.
- A written scope and quote before any work begins.