LLM application testing
Compare generated answers, extraction, and classification against task-specific criteria. Check meaning as well as format.
AI & LLM evaluation services
AI & LLM evaluation.
Evidence you can inspect.
AI evaluation services for LLM applications, RAG systems, and tool-using agents. We test representative cases, investigate failures, and hand your team a repeatable evaluation with a clear next decision.
01What we evaluate
Test the system your users experience.
RAG evaluation
Separate missing evidence, irrelevant retrieval, unsupported claims, and outdated sources. Evaluate the answer and the evidence behind it.
AI agent evaluation
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 studySample AI evaluation report: criteria, findings, and handoff →What to include in an AI evaluation scope and handoff →Need security and data-handling checks? Explore the AI SaaS audit →How to evaluate an LLM application before release →RAG evaluation metrics →AI agent evaluation →Choosing an LLM evaluation framework →Calibrating LLM evaluators →LLM testing and prompt evaluation →Monitoring and evaluation dashboards →
05Before we start
A few useful answers.
What do you need to scope AI assessment services?
Start with one user task, the decision you need to make, representative examples, and the failures that matter. We agree the system boundary, access, data-handling requirements, review process, and deliverables before work begins. Share sensitive examples only through the agreed process.
What will our team receive?
The agreed handoff can include a versioned case set, scoring rubric, baseline outputs, failure analysis, and rerun instructions. We connect findings to a next experiment and record unresolved questions. A Reliability Sprint also includes one targeted improvement experiment within its agreed scope.
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.
07Start the conversation
Start with
a scoping call.
Tell us what you’re building and the decision you need to make. If there’s a fit, we’ll email you to arrange a 20-minute scoping call. No system access is needed for this first conversation.
What we’ll discuss
evals@rubrex.ai - Your use case and the problem you’re seeing.
- The evidence you have and what to test first.
- Whether a Sprint, audit, or engineering engagement fits.
WHAT HAPPENS NEXT
- We review your request and whether we can help.
- If there’s a fit, we agree a call time with you by email.
- A written scope and quote before any work begins.