Field AI
FieldAI builds general-purpose robot intelligence through its Field Foundation Models (FFMs) and EDGE brain — a new class of physics-first, risk-aware foundation models for embodied AI. FieldAI's embodiment-agnostic autonomy lets robots navigate dynamic, unstructured environments without maps, GPS, or predefined trajectories, and has been proven across quadrupeds, humanoids, wheeled robots, and passenger-scale vehicles. Founded in 2023 (formerly AI for Humanity) and based in Mission Viejo, California, the company is led by veterans from DeepMind, Google Brain, Tesla Autopilot, NASA JPL, and SpaceX, and serves construction, energy, mining, logistics, manufacturing, power and utility, agriculture, and federal applications. FieldAI has raised over $405M from investors including Bezos Expeditions, Canaan Partners, Khosla Ventures, Temasek, Intel Capital, Hyundai Motor Company, and Prysm Capital.
Index entry only — little beyond a description and a link, and nothing machine-readable enough for an agent to act on without a human reading the site first.
API Evangelist profiles Field AI the way a machine reads it — 1 machine-readable artifact, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Field AI scores 12.3/100 (minimal), with a separate agent-readiness read of 0/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Field AI
Each block below is one kind of artifact we hold for Field AI. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
Security Posture 1
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Resources
Every other property we hold for Field AI — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Access & Security 1
Authentication, authorization, and security posture
Operate 1
Status, limits, changes, and where to get help
Commercial 2
Pricing, plans, and the legal terms of use
Company 3
The organization behind the API
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