Company comparison
Arcee AI vs Agility Robotics
Radar profile, momentum bars, and stack placement side by side.
Arcee AI
Not scored
momentum
Agility Robotics
Not scored
momentum
Arcee AI capital
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Agility Robotics capital
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No radar series.
Momentum head-to-head
Attribute tape
| Field | Arcee AI | Agility Robotics |
|---|---|---|
| Category | Uncategorized | Uncategorized |
| Stack | — | — |
| HQ | United States | Corvallis, OR, United States |
| Founded | — | — |
| Status | Operating | Operating |
| Funding | — | — |
| Momentum | — | — |
| Summary | Arcee AI is an independent AI lab that builds and releases open-weight large language models, most notably the Trinity family — a 400B sparse MoE architecture with 13B active parameters per token — spanning chat, reasoning, coding, function-calling, and agentic workflows. Its models are available via its own API and through third-party gateways such as OpenRouter. | Agility Robotics designs and deploys Digit, a commercially available humanoid robot built for repetitive work in warehouses and manufacturing facilities. Its AI stack spans robot perception, reinforcement-learning-based skill training, sim-to-real transfer, and Arc — a cloud platform for managing Digit deployments at scale. |
| Who for | Teams evaluating AI vendors in this category. | Teams evaluating AI vendors in this category. |
| Differentiator | Arcee AI is an independent AI lab that builds and releases open-weight large language models, most notably the Trinity family — a 400B sparse MoE architecture with 13B active parameters per token — spanning chat, reasoning, coding, function-calling, and agentic workflows. Its models are available via its own API and through third-party gateways such as OpenRouter. | Agility Robotics designs and deploys Digit, a commercially available humanoid robot built for repetitive work in warehouses and manufacturing facilities. Its AI stack spans robot perception, reinforcement-learning-based skill training, sim-to-real transfer, and Arc — a cloud platform for managing Digit deployments at scale. |