01
Expert task design
Create difficult, domain-faithful tasks with constraints, rubrics, and verifiable outcomes.
AI data + evaluation · Expert-led delivery
Create the expert signal better AI needs.
Design expert-authored tasks, capture high-quality work trajectories, and evaluate models against long-horizon work that reflects the real world, not only a static answer key.
Delivered by experienced technical and domain specialists with expert review.
Expert-authored tasks
Agent trajectories
Benchmarks + evals
Human quality review
The same delivery discipline behind Expert Hire
From answers to work traces
Data that captures how experts solve difficult work.
High-value models need more than scraped text and simple prompts. Expert Hire runs managed data and evaluation programs for technical, multi-step work using domain experts, explicit rubrics, reproducible environments, and review at every critical handoff.
02
Trajectory collection
Capture the decisions, tool use, revisions, and recovery steps between prompt and final result.
03
Training data programs
Curate supervised examples and structured work traces for targeted model behaviors.
04
Benchmarks and evaluations
Measure quality on repeatable tasks, hidden checks, and decision rubrics built around real work.
05
Quality and provenance
Track authorship, review status, failure reasons, and acceptance evidence across the dataset.
06
Failure analysis
Turn evaluator disagreement and model failure modes into the next task, rubric, or data improvement.
A managed expert-data loop
Connect domain expertise, data operations, and model evaluation.
The offering is designed around a closed quality loop: task definition informs collection, evaluation exposes failure modes, and those failures shape the next data release.
What usually breaks
- Generic prompts with ambiguous success
- Final answers without decision traces
- Quality review separated from collection
What we design instead
- Tasks grounded in real expert work
- Reviewable trajectories and artifacts
- Evaluation feeds the next data iteration
How delivery works
Start with the operation. Then design the team.
- 01
Define the target behavior
Choose the domain, environment, model behavior, failure modes, and acceptance evidence.
- 02
Design tasks and rubrics
Build tasks that are difficult for the right reasons and scoreable by both checks and expert judgment.
- 03
Collect and review
Capture work traces, artifacts, and annotations with expert review and clear provenance.
- 04
Evaluate and iterate
Run model trials, analyze failures, and improve the tasks, rubric, or dataset for the next release.
Ways to engage
Shape the service around the outcome.
Begin with the smallest model that can responsibly own the work. Expand only when the operating evidence supports it.
Start
Evaluation sprint
A small, carefully bounded task suite to test feasibility and expose the hardest quality questions.
Best for: A new domain, model, or agent behavior
Build
Dataset program
A governed collection program with expert contributors, review stages, and release criteria.
Best for: Training and fine-tuning initiatives
Improve
Continuous evaluation
A living benchmark and failure-analysis loop aligned to model and product releases.
Best for: Production AI systems and agents
Designed forAI labsFoundation-model teamsApplied AI companiesEnterprise AI programs
Selected organizations across the Expert Hire ecosystem
FAQ
Before we scope the work.
A practical first step
Start with one hard, measurable AI task.
Bring the target behavior, domain, and current failure mode. We will shape a focused expert-data or evaluation program around measurable acceptance evidence.