Important decisionsneed reliable AI.

When a model enters the world, its answers become someone’s next step.

Training data is human.Reliability is built at scale.

Expert judgment, made consistent across millions of examples.

Inertia is the foundrybehind the frontier.

Training data. Human feedback. Evaluations. The work beneath the model.

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NORTHGATE Calderon HELIOSTAT Rowan & Vale MERIDIAN LABS Ashcroft KESTREL

Artificial IntelligenceReal Autonomy

The real world doesn’t happen in simulation

Simulation permutes the scenarios you already know about. Real operations discover the ones you don’t — and those are the cases a deployment is judged on.

01Every world model is trained on someone’s real data. Organic capture sits upstream of synthetic data — it isn’t competing with it.
02Most public datasets record what a scene looked like. Pairing that with what the human operator did about it is the raw material of imitation learning.
03Coverage doesn’t need every machine in the world — a single well-instrumented operation produces examples worth buying.
04Anonymization at the edge: faces and identifiers blurred at capture, records clipped, consent built into the agreement.

City street scene half real photography, half dissolving into a point-cloud simulation

One foundation.
Three ways to build.

Start with the data.
Keep the human standard.

Capture

Data Engine

Expert annotation and NLP corpora, built around your domain, schema, and standard of evidence.

Build your dataset

Refine

Evals & RLHF

Model grading, preference data, and red-teaming that make failure modes visible before deployment.

Define your standard

Deliver

Deploy

Fine-tuning and domain agents that carry expert judgment into the workflows where it is needed.

Move into production

The human layer, at scale

2.6PBPetabytes labeled

84k+Expert contributors

112Languages covered

31MEvaluation tasks run

From the field to the dataset

An operation runs its normal day. Capture decides which seconds matter, and the pipeline turns them into training examples with full provenance.

01Record what the environment looked like and what the operator did, synchronized, continuously, at the edge.
02Trigger on the moments that matter — hard stops, near misses, disagreement — not on hours of routine.
03Anonymize at ingest, then label to your schema and scenario taxonomy.
04Deliver scenario-tagged datasets with the chain of custody attached.

Annotated street scene with detection boxes

The last mile is still people

Full autonomy is the wrong target for most regulated deployments. The right one is a system that knows when to stop and ask — and a review layer fast enough that asking is not a bottleneck.