# Emmi Ai

**Canonical:** https://apis.io/providers/emmi-ai/  
**Website:** https://emmi.ai  
**APIs profiled:** 0

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.

## Kin Score — 26.7 / 100 (emerging)

Scored 2026-08-17 under rubric 0.11.0. Trend: flat (+0.0 from 26.7).

| Facet | Score |
|---|---|
| Discoverability | 57.4 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Operational Transparency | 21.1 |
| Developer Ergonomics | 54.3 |
| Commercial Clarity | 36.8 |

## Agent readiness — 0.0 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| MCP Server | no |
| Auth Clarity | no |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |

## Access

Unknown — onboarding: unknown, pricing: unknown, trial: no (confidence: low).

## Security (2)

- **Emmi Ai Domain Security** — TLSv1.3 · HSTS
- **Emmi Ai Trust Center** — SOC 2 Type 2

## Tags

Company, Engineering AI, Physics Simulation, Machine Learning, Deep Learning, Scientific Computing, CAE, CFD, Manufacturing, Open Source

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/emmi-ai/). Scores are computed from the provider's own public artifacts under a published rubric.