About Rubrex
Close to the work.
Accountable for it.
Rubrex is a specialist AI evaluation and engineering firm. We define evaluation criteria, examine real failure cases, run comparisons, and hand over artifacts your team can keep using.
Our team’s experience includes AI training and evaluation, AI product development, and the infrastructure that runs those products. We work with AI product teams and engineering leaders who need a clearer basis for their next decision.
How we work
Every engagement begins with a written scope and quote. We agree system access, tools, data handling, and expected deliverables before work starts. The AI Reliability Sprint is our focused starting engagement; production pilots and engineering pods are scoped separately.
Explore AI evaluation services or read about engineering engagements.
Our AI SaaS security audits extend that evaluation discipline to application access, agent boundaries, and customer-data practices. Security work is scoped separately, with documented evidence and limitations.
Work with us as an expert evaluator
Bring subject expertise, clear reasoning, and a careful approach to evidence. Explore evaluator work and introduce yourself.
What our public evidence shows
Our rubric-generator case study describes internal work. ResumizeAI is a product built by the Rubrex team. These are clearly distinguished from external client endorsements, and we do not convert test counts or prompt fixes into unmeasured accuracy claims.
Our editorial approach
Rubrex research briefings combine concise summaries of primary sources with practical evaluation recommendations. They are not peer-reviewed papers, systematic literature reviews, or reports of experiments performed by Rubrex unless explicitly stated.
The current collection was prepared with AI-assisted research and drafting. The byline identifies Rubrex as the publishing organization; it does not claim independent human peer review. The articles separate reported findings, proposed methods, and illustrative examples.
Sources and freshness
We prioritize original research, official technical documentation, and directly attributable engineering guidance. Each briefing records when its sources were checked and identifies whether the review covered an abstract, publication record, documentation, or a technical article. Recent arXiv papers are labeled as preprints; their results remain limited to their study conditions.
A recent access date does not make an older study new. Foundational material is retained where relevant. Article modification dates change when the content changes meaningfully, not merely to make it appear fresh. A source check is a dated snapshot, not continuous monitoring.
Examples and limitations
Hypothetical scenarios and calculations are labeled illustrative. They are not customer results. Recommendations should be tested against the actual system, data, and decision requirements. Public benchmark results are not presented as predictions of a reader’s production performance.
Corrections
Send the article URL, the claim in question, and supporting evidence to evals@rubrex.ai. Corrections should update the relevant claim, citation, and article modification date together.