LLM Evaluation & Red Teaming Services
Human evaluation of your model or AI product by vetted native speakers and domain experts: accuracy, hallucination, safety and fluency, scored against your rubric. Free pilot before you commit.
Why human LLM evaluation still matters
Automated benchmarks and LLM-as-judge scores are cheap, but they miss the failures your users notice: a confident wrong answer in a regulated domain, an unnatural phrase in a local dialect, a safety bypass phrased in slang. LLM evaluation services put trained humans in front of your model's real outputs and turn their judgements into numbers you can track release to release.
AI Taggers builds evaluation sets and runs human scoring for chatbots, RAG systems, agents and foundation models. Reviewers are matched to your domain and language, work to a rubric we calibrate with you, and every score carries agreement data so you know how much to trust it.
For safety work we run structured red teaming: adversarial prompts written by native speakers across harm categories, jailbreak attempts and multilingual bypasses, logged with severity and reproduction steps.
Evaluation work we run
Response quality scoring
Correctness, helpfulness, completeness and instruction-following, scored per response against your rubric.
Hallucination & factuality checks
Claim-level fact checking by domain reviewers, including citation verification for RAG answers.
RAG & agent evaluation
Retrieval relevance, groundedness and task-success judgements for retrieval pipelines and multi-step agents.
Red teaming & safety
Adversarial prompt sets, jailbreak testing and harm-category coverage, with severity ratings and reproducible logs.
Multilingual & dialect evaluation
Fluency, register and cultural fit judged by native speakers, including Arabic dialects, Turkish, Hebrew, Persian and Urdu.
Side-by-side model comparison
Blind A/B evaluation of model versions or vendors, with win rates and confidence intervals.
How a project runs
- 1
Define what good looks like
We turn your product requirements into a scoring rubric and a test-set plan covering the cases that matter.
- 2
Free pilot
Reviewers score 25-50 of your model outputs within 24-48 hours, so you can check their judgement against your own.
- 3
Calibrate
Disagreements are resolved into rubric changes and calibration examples before the full run.
- 4
Evaluate and report
Full evaluation with agreement metrics, failure categories and examples, repeatable for every release.
How evaluation pricing works
Evaluation is priced per scored item or per reviewer-hour for open-ended red teaming. We quote after the free pilot, once the rubric and average task time are known.
Typical per-unit rates for image, text, audio and video work are on our pricing page.
Best fit for
- Product teams launching an LLM feature in a regulated or high-stakes domain
- Model builders needing human evals in non-English markets
- Teams comparing models or vendors before committing
- Safety and trust teams needing multilingual red teaming
Frequently asked questions
What is included in LLM evaluation services?
A rubric agreed with you, a test set (yours or one we build), human scoring by matched reviewers, agreement metrics, and a report of failure categories with examples. Red teaming adds adversarial prompt writing and severity-rated findings.
Can't we just use LLM-as-judge?
Automated judges are useful for scale, but they share blind spots with the models they grade, especially in other languages and specialist domains. Most teams use a human-scored set to calibrate and spot-check their automated judge.
Do you evaluate in Arabic and other languages?
Yes. Native-speaker evaluation is a core specialism, including Gulf, Egyptian, Levantine and Maghrebi Arabic, so you can see how your model handles real dialect usage rather than Modern Standard Arabic only.
How do you keep our model outputs confidential?
Work runs under NDA with access limited to the assigned reviewers, and you control data retention and deletion. Onshore Australian handling is available for sensitive projects.
How quickly can you deliver?
Pilot results are usually back in 24-48 hours. Full evaluation runs depend on volume and reviewer specialism; recurring release evaluations can be scheduled to your release cycle.
Get a free evaluation pilot
Send 25-50 model outputs and what 'good' means for your product. We score them in 24-48 hours so you can compare our reviewers' judgement with your own.
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