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Covariant website screenshot

Covariant

Covariant is an Emeryville, California warehouse-robotics AI company founded in 2017 (originally as Embodied Intelligence) by UC Berkeley professor Pieter Abbeel and his former students Peter Chen, Rocky Duan, and Tianhao Zhang. The company builds the Covariant Brain, a universal AI platform that enables industrial picking robots to handle virtually any SKU regardless of shape, size, or packaging, and in March 2024 it introduced RFM-1 (Robotics Foundation Model 1), an 8-billion-parameter multimodal transformer trained on text, images, video, robot actions, and numerical sensor readings to give robots human-like reasoning over the physical world. Covariant ships AI-powered solutions for order picking, sortation, item induction, kitting, depalletization, and goods-to-person workflows, delivered through long-running integration partnerships with KNAPP (Pick-it-Easy Robot, since 2020) and ABB (warehouse robotics, since 2020), as well as deployments with retailers and logistics providers worldwide. In August 2024, Amazon announced a non-exclusive license to Covariant's robotic foundation models and hired its three co-founders along with roughly a quarter of its workforce in a reverse-acquihire arrangement; Covariant continues to operate independently under COO-turned-CEO Ted Stinson and co-founder Tianhao Zhang. The Covariant Brain and RFM-1 are proprietary, deployed on-premise on partner robotics hardware, and the company has no public developer API, SDK, open-source release, or third-party developer program.

human only

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.

Kin Score

API Evangelist profiles Covariant the way a machine reads it — 2 machine-readable artifacts, 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 — Covariant scores 12.4/100 (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 Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5
Composite quality — 12.4/100 · minimal
Contract Quality 5.2 / 25
Developer Ergonomics 0.4 / 20
Commercial Clarity 0.0 / 20
Operational Transparency 0.0 / 13
Governance 0.0 / 12
Discoverability 6.8 / 10
Agent readiness — 0/100 · human only
Machine-Readable Contract 0 / 18
Agentic Access Contract 0 / 15
MCP Server 0 / 12
Machine-Readable Auth 0 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 0 / 7
Typed Event Surface 0 / 6
Agent Skills 0 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3

How we profile Covariant

Each block below is one kind of artifact we hold for Covariant. 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.

Semantic Vocabularies 1

JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.

JSON-LD contexts and semantic vocabularies used across these APIs.

Covariant Context

5 classes · 19 properties

JSON-LD

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.

Covariant Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Every other property we hold for Covariant — 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.

Access & Security 1

Authentication, authorization, and security posture

Operate 1

Status, limits, changes, and where to get help

← All providers · Data indexed from github.com/api-evangelist/covariant · machine-readable index on apis.io