FRONTIER AI RESEARCH

Human data and evaluation for frontier models

ArcticMind runs rigorous human-data programs for model evaluation, alignment, and post-training

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Static benchmarks miss the failures that matter

Everyone in this market says the same things. Then the platform breaks mid-session, the recording is incomplete, the labels don't hold up to scrutiny, and the export takes your team a week to untangle. Meanwhile the people in the seat all look the same, so your model learns one market's habits and calls it the world. We built our operation around the two things that actually matter: reliable collection and real diversity of who's doing the tasks.

Human-data systems for evaluation, alignment, and post-training

Evaluation with real people

Put your model in front of the people it's actually built for and find out where it breaks.

Side-by-side comparisons · Blind grading · Expert review · Error analysis · Clear rubrics

Training data

Fill specific capability gaps with examples, corrections, and judgments from the right people.

Demonstrations · Preference judgments · Critiques and corrections · Specialist annotation

Agent and workflow data

Watch people and models work through real multi-step tasks, and capture everything that happens along the way.

Long tasks · Full session recording · Tool use · Where things go wrong and how people recover

How people actually use AI

Study how different kinds of users trust, check, ignore, or override what a model tells them.

Trust and reliance · Differences across user groups · Misuse testing · Changes over time

Why Arctic

Arctic combines rigorous experimental design with the operational ability to generate difficult human and behavioral data at scale. Our team has designed more than 20,000 experiments and delivered complex programs for frontier AI teams, Fortune 500 companies, and highly regulated industries.

Real people, verified

Everyday consumers, trained taskers, working professionals, deep specialists. Screened against your criteria, not self-reported checkboxes.

The whole session, not just the answers

We capture what people saw, what they did, where they hesitated, and how it ended. You get the full picture, not a final label with no context.

Any market, any segment

Over 100 million reachable people across countries, languages, ages, and professions. If your model needs to work for someone, we can put that someone in the seat.

Built for empirical model research

Start with the uncertainty. We configure the cohort, task distribution, environment, telemetry, and evaluation protocol around it.

Full-trace evaluation

Build a realistic version of the workflow, run models and people through it, and see where the agent completes, stalls, or needs a human.

Verified human intelligence

Recruit consumer populations, AI taskers, specialized professionals, and domain experts against explicit screening criteria.

Realistic task environments

Run studies in purpose-built tasks, enterprise workflows, consumer products, games, and historical performance archives.

Reproducible releases

Ship versioned datasets, task specs, rubrics, grader configs, provenance, limitations, and strict train/eval separation.

HOW IT WORKS

Research specification -> validated release

Start with a question

What do you need to know, and what decision does it feed? We design backwards from that.

Build the study

We pick the people, the tasks, the environment, and the grading standard, and agree on what "good" looks like before anything runs.

Run and iterate

Sessions run, graders get calibrated, disagreements get resolved, and we flag problems while there's still time to fix them.

Usable deliverables

Datasets, methods, grading specs, findings, and honest limitations. Ready to use the day it lands.

Get Started

Bring us the question, a gap in your evals, a weakness you want to train against, an agent that falls over in the real world, or a population you can't reach.

We'll design the study and get you the data.

Talk to us about a study

Sessions run, graders get calibrated, disagreements get resolved, and we flag problems while there's still time to fix them.