Emmi Ai

Emmi AI is an Austrian engineering-AI company building Large Engineering Models (LEMs) - pre-trained, physics-accurate neural networks that replace traditional CAE/CFD solvers to deliver real-time, GPU-accelerated simulation and design validation for manufacturing, aerospace, semiconductor, and energy engineering. Its flagship open-source Noether framework (the emmiai-noether PyTorch package) provides transformer building blocks, a model/dataset/recipe zoo, and a command-line toolchain for training, fine-tuning, and deploying industrial physics models, alongside vertical products such as NeuralWing (aircraft wing validation), NeuralMould (injection moulding), and NeuralDEM (particulate flows). Emmi AI was acquired by Mistral AI in May 2026 to build an industrial AI stack.

Emmi Ai is profiled on the APIs.io network. Tagged areas include Company, Engineering AI, Physics Simulation, Machine Learning, and Deep Learning.

Emmi Ai’s developer surface includes documentation, API reference, getting-started guide, engineering blog, support, CLI, changelog, and 19 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

31.8/100 thin ▬ flat Agent 8/100 agent aware saas Full breakdown ↓
scored 2026-10-17 · rubric v0.24.0

AccessApproval

0 APIs

CompanyEngineering AIPhysics Simulation Machine Learning Deep Learning Scientific Computing CAE CFD Manufacturing Open Source

Kin Score

Kin Score How this is scored →
scored 2026-10-17 · rubric v0.24.0

Composite quality — 31.8/100 · thin

Contract Quality 0.0 / 21

Developer Ergonomics 11.3 / 17

Access Clarity 6.3 / 17

Operational Transparency 2.0 / 11

Contract Governance 1.9 / 10

Discoverability 4.7 / 9

Regulatory Posture 3.3 / 15

Agent readiness — 8/100 · agent aware

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

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 7 / 7

Typed Event Surface 0 / 6

Agent Skills 5 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 3 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from github.com/api-evangelist/emmi-ai: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Markets this provider is in provisional

The Kin Score Market layer reads a provider inside its market: its position, and how deep its integrations go. Depth is only comparable within a position. Provisional and unweighted: not part of the Kin Score or agent readiness.

Market	Position	Depth	Registered MCP server	Declares compatibility
Engineering CAD, CAE and PLM	distribution	9.6 (thin)	-	-

Registered MCP server and declares compatibility are report only — not weighted. Undeclared means we found nothing we can read, so there is no number: we never infer integrations.

Pricing Plans 1

Published pricing tiers and plan structures.

Emmi Ai Plans Pricing

0 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Emmi Ai Rate Limits

0 limits

RATE LIMITS

Security Posture 2

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Emmi Ai Domain Security

TLSv1.3 · HSTS

SECURITY

Emmi Ai Trust Center

SOC 2 Type 2

SECURITY

Resources

Get Started 2

Portal, sign-up, and the first successful call

DeveloperPortal

DeveloperPortal

GettingStarted

GettingStarted

Documentation 2

Reference material describing how the API behaves

Documentation

Documentation

APIReference

APIReference

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

AgentSkill

AgentSkill

Design & Contract 2

Pagination, idempotency, versioning, errors, and events

Lifecycle

Lifecycle

Conformance

Conformance

Build 5

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

Packages

SDKs

CLI

SourceCode

Access & Security 3

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

TrustCenter

TrustCenter

Compliance

Compliance

Operate 3

Status, limits, changes, and where to get help

Support

Support

ChangeLog

ChangeLog

RateLimits

RateLimits

Commercial 4

Pricing, plans, and the legal terms of use

PrivacyPolicy

TermsOfService

Plans

License

Company 2

The organization behind the API

Website

Website

Blog

Blog

Other 1

Properties that don't map to a standard resource type

ContentSignal

ContentSignal

Source (apis.yml)

aid: emmi-ai
deliveryModel:
  model: saas
  license_evidence:
    not_product:
    - emmi-ai/noether
  open_source: unknown
  commercial: true
  callable_host: false
  label: Hosted service · you call their endpoint
  confidence: low
  source:
  - pricing
  - repository-not-product
  generated: '2026-09-25'
  method: derived
accessModel:
  pricing: unknown
  onboarding: approval
  trial: false
  try_now: false
  public: false
  label: Requires approval
  confidence: low
  source:
  - plans
  - rate-limits
  - security
  generated: '2026-09-03'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/emmi-ai.png
name: Emmi Ai
description: Emmi AI is an Austrian engineering-AI company building Large Engineering Models (LEMs) - pre-trained, physics-accurate
  neural networks that replace traditional CAE/CFD solvers to deliver real-time, GPU-accelerated simulation and design validation
  for manufacturing, aerospace, semiconductor, and energy engineering. Its flagship open-source Noether framework (the emmiai-noether
  PyTorch package) provides transformer building blocks, a model/dataset/recipe zoo, and a command-line toolchain for training,
  fine-tuning, and deploying industrial physics models, alongside vertical products such as NeuralWing (aircraft wing validation),
  NeuralMould (injection moulding), and NeuralDEM (particulate flows). Emmi AI was acquired by Mistral AI in May 2026 to build
  an industrial AI stack.
url: https://raw.githubusercontent.com/api-evangelist/emmi-ai/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- speedinvest
x-acquired-by: mistral-ai
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-08-17'
x-docs-platform: sphinx
x-docs-platform-confidence: medium
x-docs-platform-checked: 2026-10-03
tags:
- Company
- Engineering AI
- Physics Simulation
- Machine Learning
- Deep Learning
- Scientific Computing
- CAE
- CFD
- Manufacturing
- Open Source
tags_raw:
- Company
- Engineering AI
- Physics Simulation
- Machine Learning
- Deep Learning
- Scientific Computing
- CAE
- CFD
- Manufacturing
- Open Source
- Machine-Learning
- Open-Source
apis: []
common:
- type: DomainSecurity
  url: security/emmi-ai-domain-security.yml
- type: Website
  url: https://emmi.ai
- type: DeveloperPortal
  url: https://noether-docs.emmi.ai/
- type: Documentation
  url: https://noether-docs.emmi.ai/
- type: APIReference
  url: https://noether-docs.emmi.ai/autoapi/noether/index.html
- type: GettingStarted
  url: https://noether-docs.emmi.ai/tutorials/getting_started_install_and_verify.html
- type: GitHubOrganization
  url: https://github.com/Emmi-AI
- type: Blog
  url: https://emmi.ai/news
- type: Support
  url: https://emmi.ai/contact-us
- type: PrivacyPolicy
  url: https://emmi.ai/privacy-policy
- type: TermsOfService
  url: https://emmi.ai/imprint
- type: Packages
  url: packages/emmi-ai-packages.yml
- type: SDKs
  url: packages/emmi-ai-packages.yml
- type: CLI
  url: cli/emmi-ai-cli.yml
- type: ChangeLog
  url: changelog/emmi-ai-changelog.yml
- type: LLMsTxt
  url: llms/emmi-ai-llms.txt
- type: TrustCenter
  url: security/emmi-ai-trust-center.yml
- type: Compliance
  url: https://emmi.ai/security-compliance
- type: Lifecycle
  url: lifecycle/emmi-ai-lifecycle.yml
- type: Plans
  url: plans/emmi-ai-plans-pricing.yml
- type: RateLimits
  url: rate-limits/emmi-ai-rate-limits.yml
- type: Conformance
  url: conformance/emmi-ai-conformance.yml
- type: AgentSkill
  url: skills/_index.yml
- type: ContentSignal
  url: well-known/emmi-ai-robots.txt
- type: SourceCode
  url: https://github.com/Emmi-AI/noether
- type: License
  url: https://raw.githubusercontent.com/Emmi-AI/noether/main/LICENSE.txt
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-08-17'
  status: enriched
  artifacts_added: 7
  pass: local-v1
x-coverage:
  state: none
  reason: no-developer-program
  detail: 'Emmi AI ships an open-source Python framework (emmiai-noether) and a CLI that run on the customer''s own CPU/GPU/SLURM
    hardware - there is no hosted service and no API: no api./app./platform./console./mcp. host resolves under emmi.ai, and
    every contract-discovery probe (openapi.json, agent-card.json, agent.json, every /.well-known/ path on both emmi.ai and
    noether-docs.emmi.ai) returned 404 or NXDOMAIN.'
  evidence:
  - url: https://emmi.ai/openapi.json
    status: 404
  - url: https://emmi.ai/.well-known/agent-card.json
    status: 404
  - url: https://noether-docs.emmi.ai/.well-known/agent.json
    status: 404
  - url: https://emmi.ai/api
    status: 404
  - url: https://pypi.org/pypi/emmiai-noether/json
    status: 200
  checked: '2026-08-17'

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