Better data for training. Apps for technical work.
Human Data gives teams expert-made data for model training. App Data gives teams focused apps for model launches, robot runs, synthetic datasets, diligence files, risk cases, scans, lots, candidates, sequences, and routes.
Human Data gets training data from experts. App Data runs focused apps for technical work: model launches, robot runs, synthetic datasets, drug programs, financial cases, scans, lots, candidates, sequences, and routes.
Find the people who know the work. They create, label, rank, write, and evaluate the examples your model learns from.
Choose the app for the job. Models checks a release. Robotics prepares robot runs. Synthetic Data builds a dataset. Each app states the input and the output you get back.
Why now: the largest data vendor was absorbed by one of the labs it served. A competitor lost four terabytes — including who its workers were. The EU AI Act's training-data rules enforce in August 2026, and 78% of teams can't validate their data before training. You need a neutral source you can defend under audit.
The right people create, label, grade, and review the examples your model learns from.
KeptThe work is scoped for your program, with the person, task, and usage terms kept together.
KeptYour data is built for your model and your standards. It is not mixed into a shared vendor pool.
KeptRun the model on real work. Let experts check what it gets wrong. Turn the misses into better tests and better training data.
Test the model.
Check the edge cases.
Bring in the right expert.
Remember the misses.
Ship what works.
Every miss becomes a sharper test.
Models checks one release. Robotics prepares robot runs. Synthetic Data builds a dataset from a schema. Drug Development organizes a program file. Financial prepares a risk case. Imaging prepares a study file. Each app has a clear input and output.
See App DataTest one model release before it ships.
Open appTurn real-environment capture, teleop interventions, and robot failures into consent-scoped datasets your robotics team can use.
Open appRun asset scouting, scientific diligence, and clinical strategy on one clear record.
Open appGenerate synthetic coverage your team can test before it reaches training or evaluation.
Open appPrepare risk cases and explain the edge cases before an examiner or committee asks.
Open appPrepare scans, flag ambiguous studies, and move forward with a study-ready file.
Open appCheck inspections and decide what can ship with the file attached.
Open appScreen material candidates and move only the right ones into qualification.
Open appScreen risky sequences before they reach synthesis or the bench.
Open appCompare routes and choose the path the bench can actually defend and run.
Open app“Release prep moves from a late scramble to a repeatable path. When an edge case slips, the team captures it, names it, and turns it into the next check.”
We scope the dataset, app, input, and output, then show what your team gets back. A robotics project starts with one prepared batch of robot runs.