06 / Capability
TazQ provides AI research services: custom benchmarks designed by domain experts, LLM judges calibrated against human judgment, and expert review of synthetic data. Research teams use them to measure capabilities that existing benchmarks miss and to trust the automated evaluation and data they scale with.
Definition
AI research services provide the human expertise behind measuring and improving frontier models. They include designing new benchmarks around real research questions, calibrating LLM-as-a-judge systems so their scores match expert judgment, and reviewing synthetic data so that what a model learns from is accurate and useful.
Why it matters
When models outgrow existing tests, research depends on measurements experts can trust.
Popular tests get solved or leak into training data, and stop telling you what a model can really do.
LLM judges are fast but carry their own biases. Without human calibration, their scores can quietly mislead.
Generated data scales easily but can be subtly wrong. Expert review keeps errors from compounding in training.
How TazQ does it
Every research project follows four steps, shaped around the question you need answered.
Agree what capability or behaviour you need to measure, and what a meaningful result would look like.
Domain specialists write original, hard tasks with reference answers and scoring guidance, kept private to avoid contamination.
Expert judgments set the ground truth, and LLM judges are tuned and checked until they agree with it.
Items, judges and synthetic data are reviewed for quality and difficulty, then delivered with full documentation.
Deliverables
Formats adapt to your research stack. These are the outputs research teams ask for most.
| Data | What it includes | Typical formats |
|---|---|---|
| Custom benchmarks | Original, expert-written tasks with reference answers, difficulty ratings and scoring guidance. | JSONL items with documentation |
| Human baselines | Expert scores on the same tasks, so model results can be compared with skilled people. | Scored results with analysis |
| Judge calibration sets | Expert-labelled examples used to tune and test LLM judges. | JSONL with labels and rationales |
| Judge agreement reports | How closely an LLM judge matches expert judgment, and where it disagrees. | Report with examples |
| Synthetic data review | Generated examples checked by experts, with errors corrected or removed. | Reviewed JSONL with flags |
| Evaluation design | Methodology, rubrics and analysis plans for your experiments. | Written methodology |
Use cases
Hard, original tasks in specialised domains that show what the newest models can and can’t do.
Human-calibrated judges that let teams evaluate at scale without losing touch with expert standards.
Expert review that keeps large synthetic datasets accurate as they grow.
Rigorous, documented evaluation designed to support publication and reproducible results.
Quality & experts
Item writers and reviewers are specialists in their fields, prepared through TazQ Academy for each project’s methods.
Explore TazQ AcademyNew items are written from scratch, kept private and checked against public sources to avoid leakage.
Every benchmark and judge comes with its methodology, so results can be understood, reproduced and cited.
FAQ
LLM-as-a-judge is the use of a language model to score other models’ outputs. It scales evaluation, but its scores need calibrating against expert human judgment to be trusted.
Experts label a set of examples to create ground truth. The judge’s prompts and criteria are then tuned until its scores agree closely with the experts, and disagreements are analysed and reported.
Items are written from scratch by experts, kept private, and checked against public sources so that models are unlikely to have seen them during training.
Yes. Specialists check generated examples for accuracy, quality and fit with your goals, correcting or removing those that would teach a model the wrong thing.
They can. Experts complete the same tasks so model results can be compared with skilled human performance.
Share your research question, the domains involved and how you plan to use the results. A small pilot then validates the design before full development. You can outline this in the project brief below.
Related capabilities
Human evaluation that investigates hallucinations, bias, and failure modes—turning uncertain model behavior into clearer quality signals.
Expert demonstrations, reasoning traces, and human preferences that help capable models become more useful, reliable, and aligned.
Start your Research Services project
A better model starts with a clearer question.
What do you want yours to understand?
Scope your project in about a minute. A specialist reads every brief and replies with next steps.
Experts join through TazQ Academy.
The Academy prepares specialists for TazQ projects and matches them to work in their field.
Explore TazQ Academy