AI DATA & EVALUATION LAB
We build and manage training and evaluation datasets for image, video and multimodal models, combining creative domain experts with structured quality control.
DATA SOLUTIONS
We define the task, source the right experts, collect or evaluate content, and deliver versioned data ready for training, benchmarking and model release decisions.
01 / MULTIMODAL EVALUATION
Custom datasets and pointwise, side-by-side or rubric-based review show how each model performs across target skills, scenarios and content types.
View evaluation options02 / CREATIVE EXPERT DATA
Designers, photographers, editors, 3D artists and art directors produce prompts, annotations, feedback and reference content across creative modalities.
View data program03 / REWARD DATA
Pairwise comparisons, pointwise scores, critiques and corrections are reviewed for consistency and delivered in versions that match your training cadence.
View data programMANAGED DATA DELIVERY
We translate model requirements into instructions and quality criteria, qualify contributors, manage production and deliver structured data with documented review results.
Task + modality
→Relevant experts
→Training + exam
→Managed workflow
→QA + adjudication
→Versioned dataset
START HERE
Send us your agent or model. We run it through real scenarios on expert reviewers, show you exactly where it breaks, and hand back a reusable eval harness — a fixed-fee pilot, no long procurement. It's the fastest way for an ML team without an eval function to trust what ships.
A specialized MAST/TRAIL breakdown of how your system fails, each mode with frequency and the first unrecoverable step.
Every scenario labelled: outcome, trajectory match, per-rubric scores and human adjudication, delivered as data.
Accuracy per metric with intervals, inter-rater agreement and gold-set pass rates — you trust the numbers.
Ranked by frequency × severity and tied to specific failing traces — actionable, not diagnostic-only.
The assertions, calibrated judges and gold set as a regression gate you re-run every release.
Fixed-fee pilot · scoped on a call · transparent per-trajectory pricing after
DELIVERY MODELS
Start with a defined evaluation or data task, expand into recurring production, or add a dedicated expert team to an existing research workflow.
Build a dataset around specific skills, scenarios or failure modes and compare model versions with expert or automated scoring.
Run recurring collection, annotation or preference-data cycles with contributor management and multi-stage quality control.
Add a data lead, QA specialists and relevant domain professionals to support changing research priorities.
QUALITY SAMPLE / REVIEWED
QUALITY CONTROL
Quality control runs throughout production, not only at final delivery.
Reference tasks, contributor qualification, automated checks, expert review and adjudication are configured for the task and tracked across every batch.
Task rules, reviewer decisions and dataset versions remain available for audit.
AI USE CASES
Each program combines the required modality, contributor profile, evaluation method and quality workflow for the target model.
Expert feedback, multimodal content collection, professional annotation, quality filtering and side-by-side model evaluation.
Task specifications, simulated environments, expert demonstrations, trajectory review, verifiers and safety testing for tool-use workflows.
Domain-specific demonstrations, preference data, reasoning tasks, critiques and corrections for supervised and reinforcement learning workflows.
Risk-focused evaluation datasets and adversarial testing for harmful outputs, bias, policy violations and prompt-injection vulnerabilities.
INTEGRATION
Exchange tasks and results through versioned files or API endpoints, run model-assisted checks, and deliver data in the schema required by your training pipeline.
from metronlabs import Eval
run = Eval(
rubric="visual-v4",
holdout="blind",
endpoint=model_09
)
run.compare(baseline=model_08)
SECURITY / GOVERNANCE
Project-specific access, documented data flows, configurable retention and client-controlled delivery environments support research and procurement requirements.
Client cloud / VPC-ready delivery
Role-based, project-isolated permissions
Versioned rubric, rater and batch history
NDA, DPA, SOW and security pack ready
START A DATA PROGRAM
We will scope the modality, contributor expertise, volume, evaluation method, quality controls and delivery format for your project.