◆ Gentoo Logic · Modeling Warehouse

Architecture comparison

Covariant vs Figure

Covariant runs closer to the foundational model (Native (own / self-hosted weights)) than Figure (Native (own / self-hosted weights)). Shared foundation: proprietary / self-built models (highlighted).

Covariant

Robotics

Proximity
Native (own / self-hosted weights)
Models
proprietary / self-built models
Cloud · datastore
AWSobject
Compliance
SOC 2
Architecture

Warehouse robotics on RFM-1, its own robotics foundation model (team since joined Amazon).

Figure

Robotics

Proximity
Native (own / self-hosted weights)
Models
proprietary / self-built models
Cloud · datastore
Hybridproprietary manufacturing/operations data infrastructure (MES/PLM/ERP/WMS stack)[2]custom telemetry and training data pipelines for Helix VLA (vision, language, proprioception)[7][9]
Compliance
Not publicly specified; likely standard enterprise practices but no explicit SOC2/ISO27001/IEC 62443 claims in accessible materials[1][2][7][9]
Architecture

Figure’s production architecture is centered on its in-house **Helix** vision-language-action stack running on vertically integrated humanoid hardware and a proprietary BotQ manufacturing/software infrastructure, with robots streaming high-bandwidth data for continuous training and fleet improvement[1][2][7][9]. Core control and reasoning have moved off external LLMs to fully in-house Helix models, with cloud services used as supporting infrastructure rather than as foundational AI engines[7][9].

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