Embodied AI Infrastructure

The real-world data layer for embodied AI.

89% success in simulation. 12% in real homes. Stanford measured it in April 2026: humanoid robots succeed 89% of the time in simulation and 12% of the time in real homes. The gap between those two numbers is where your robot's career ends.

AlphaGen closes it — from the real side.

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89% sim

Success rate in simulation

12% real

Success rate in real homes

Stanford BEHAVIOR Challenge · April 2026

The problem

Every embodied-AI team is hitting the same wall.

Simulators look impressive. Real-world performance collapses. Amazon shelved its Blue Jay warehouse robot after six months. Humanoid manufacturers ship demos, not products. Investor patience is finite.

The diagnosis is the sim-to-real gap. The usual prescription is "more simulation." But simulators can only model the scenarios their designers imagined. Real houses, real warehouses, real kitchens are full of scenarios nobody imagined — and those are exactly the scenarios where robots fail.

The answer

Train on real-world footage, at the fidelity simulators were reaching for.

AlphaGen is the production pipeline that takes raw video of the environment your robot actually needs to operate in, and turns it into structured, frame-accurate, multi-modal training data — ready to plug into your training loop.

Frame-accurate records

Masks, 3D position, hand and body pose, depth, gaze, intent, action segments, scene-graph relationships. All temporally aligned, timestamped, and provenanced.

Human-verified quality

Human annotators resolve the ambiguity simulators hand-wave past. Every annotator is trust-scored across five dimensions; high-trust contributions get higher weight in the consensus.

A living dataset

Corrections, operator insights, and model improvements propagate automatically. The longer you run it, the sharper it gets. Nothing rots in place.

OOD awareness

The system flags every entity it has never confidently seen before, surfacing the exact frames that would trip your model in deployment. Maximum learning per annotation hour.

Multi-format export

COCO, CVAT, WebDataset, or native JSON. Or give us your schema and we'll match it. Plug into the training loop you already have.

How the system works

Six steps from raw video to training-ready data.

Ingest

Raw video goes in. Any camera, any resolution, any length.

Understand

Machine perception extracts every object, person, hand, pose, depth profile, gaze, and scene relationship frame by frame.

Verify

Ambiguous frames route to trust-scored human annotators. High-trust contributions carry higher weight in the consensus.

Synthesise

A scene-graph layer reconciles human input with the machine draft and produces the final structured record.

Learn

Every record enriches a living dataset. Improvements propagate automatically — older records get upgraded as the system sharpens.

Export

Pull structured data in any format that fits your training loop.

FAQ

Common questions

One structured record per clip. Per-frame masks, 3D positions and depth, hand and body pose, gaze and attention traces, intent and action labels, and scene-graph relationships. Exportable as COCO, CVAT, WebDataset, or native JSON — or we can match your schema.

Vision-language models give you an answer on any frame. They don't give ground truth, outputs don't compose across frames, and they have no way to flag their own uncertainty. AlphaGen produces verified, temporally-consistent records with full provenance.

Labelling services trade scale for quality. AlphaGen's infrastructure does the opposite — it lets you scale because quality is managed. Machine perception does the wide-coverage draft; humans verify exactly the frames that matter, trust-scored so the consensus is reliable.

Simulators optimise for the scenarios their designers imagined. Real-world video contains the scenarios nobody imagined — and those are exactly the ones where embodied models fail. We're the real-world complement that makes sim-trained models survive contact with deployment.

Every correction, every model improvement, every trust score re-calibration flows back through the existing corpus. A record labelled three months ago doesn't stay frozen — it gets upgraded as the system's understanding sharpens.

You do. We never re-sell, re-use, or cross-pollinate customer data. The audit trail proves it. Our commercial model is based on pipeline usage, not data monetisation.

A pilot. Send us a representative clip, we run it through, and you get back the structured record. There is no minimum volume for the pilot.

Email hello@alpha-gen.ai with a short description of the environments you're training for and a representative clip if you have one.