Four service lines, one standard: every score traceable, every verdict explainable. Each service below says what it is, how it works step by step, and what you get. Pick the one that matches your problem, or start a pilot and we will point you to the right one.
We grade outputs and build evaluation data. We evaluate what your AI says and does, from samples you provide. We do not train models, run fine-tuning, or sell experts we cannot verify.
The full human evaluation loop for model outputs. We score your model's responses against your rubric, put trained reviewers on the edge cases, and re-score every version so you see exactly what changed between releases.
What this is: every output graded against your rubric, with per-output scores and pass/fail flags. The core of everything we do.
Send 20 to 50 outputs plus your rubric, or we help you write one from your quality bar.
Trained reviewers score each output P0 to P3, with a written reason for every grade.
You get per-output grades, the failure patterns across the batch, and recommended fixes.
What this is: trained reviewers on the outputs machines should not judge alone: edge cases, flagged items, and high-stakes decisions.
We define with you what gets flagged for human review and what the reviewer checks.
Reviewers check each flagged output with written reasoning you can audit.
You get human verdicts on every flagged item, plus the cases that need your call.
What this is: every model version re-scored on the same fixed set, so you see exactly what got better or worse between releases.
We lock a fixed evaluation set that represents your real inputs.
Each new model version gets scored identically on that set.
You get version-over-version deltas: what improved, what regressed, by how much.
Calibrated judge models that score outputs at scale, in hours instead of weeks. Built with you and calibrated against human judgment, so the machine scores match what your reviewers would say.
What this is: judge models scoring thousands of outputs per run, with pass/fail flags and a human review queue for the uncertain ones. Full coverage without full human cost.
We calibrate the judge on your rubric and a sample of your outputs.
The pipeline scores every output and flags low-confidence ones for humans.
Reviewers check only the flagged queue. You get full coverage at a fraction of the cost.
What this is: the scoring rubric built with you, then calibrated until the judge agrees with human reviewers. The foundation every automated score stands on.
We draft the rubric from your quality bar and edge cases.
We measure judge-vs-human agreement and tune until it holds.
You get a rubric and a calibrated judge you can trust at scale.
Targeted audits on the failure modes that matter most: grounding, agents, safety, and hallucinations. Each audit ends with severity-graded findings and concrete fixes.
What this is: we verify your answers actually come from the retrieved documents, with citation-level attribution. For teams whose AI must not invent sources.
Send Q&A pairs with the documents your system retrieved.
We check every claim against its cited source, line by line.
You get grounding scores plus the exact unsupported claims, with what the source actually says.
What this is: we review agent traces to catch bad tool calls, loops, and unsafe actions before your users do.
Send agent traces, or give us a test harness to run against.
We grade each step: valid calls, redundant calls, loops, unsafe actions.
You get trace-level findings with severity grades and where the agent went wrong.
What this is: structured adversarial testing to find jailbreaks, leaks, and harmful outputs before your users do.
We define the threat model with you: what must never happen.
Adversarial prompts probe your model for breaks, systematically.
You get every successful break with reproduction steps and severity.
What this is: we flag made-up facts, numbers, and citations in model outputs, so nothing fabricated ships as truth.
Send outputs, with source documents if your system retrieves any.
We verify claims, numbers, and citations against sources or known facts.
You get every hallucination flagged, with what was actually true.
The human data behind better models: accurate labels and ranked preferences, delivered as datasets. We deliver the data, never the training.
What this is: accurate labeled datasets for training and evaluation, done by trained reviewers with agreement checks on every batch.
We define labels and edge-case rules with you, in writing.
Reviewers label with overlap, so we can measure agreement.
You get the dataset plus agreement scores, so you know how much to trust it.
What this is: ranked human preferences to train your reward models. Clean pairs, quality-checked, ready for training. We deliver the data, never the training run.
We collect pairwise preferences from trained reviewers on your outputs.
Quality checks filter noise, spam, and low-agreement pairs.
You get clean preference pairs in your format, ready to train on.
The prompts behind your evals and your product, written and refined with testing behind every change.
What this is: we write and refine the prompts behind your evals and your product, and prove each change with before-and-after eval scores.
Send your current prompts and what is going wrong.
We rewrite and test variants against your eval set.
You get the improved prompts with the score deltas to prove it.
20 to 50 outputs and your rubric, or we help you write one. Everything stays confidential.
Per-sample grades, failure patterns across the batch, recommended fixes, and a walkthrough call.
Re-score every release on the same set. Know exactly what changed, every time.
Describe your problem in the pilot form and we will point you to the right one. As a rough guide: wrong or risky outputs means LLM Evaluation and QA; too many outputs to grade by hand means Automated Evaluation; a specific fear (jailbreaks, invented citations, bad agent actions) means a Specialized Audit; and if you need human data to train on, that is Training Data.
No. We grade outputs and build evaluation data, including preference data you can train on yourself. Training runs and fine-tuning are not services we offer, and we will tell you that plainly rather than pretend otherwise.
A typical pilot runs one to two weeks from samples to graded report. Automated pipelines score thousands of outputs in hours once calibrated.
Outputs and a rubric, or just outputs and we help you write the rubric. Nothing else is needed to start a pilot.
Yes, and most pilots do. A common shape is automated scoring for coverage plus human review on the flagged queue, with a red-team audit on the side. Tell us the problem and we will scope the combination.
Tell us which service matches your problem, or just describe the problem. We will take it from there.
A 60-second tour of VideoEval scoring 10 AI-generated clips: prompt adherence, color saturation, motion ghosting, pass/fail verdicts, and the human review queue. Real demo, real numbers.
JudgeMyAI