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README.md

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 # NXAI 
 **Efficient Industrial AI at the Edge**
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 ## Our Mission 
 At NXAI, we translate advanced AI research into market-ready, high-efficiency solutions for industrial and embedded systems. We bridge the gap between academia and industry, empowering sectors such as manufacturing, engineering, logistics and energy with scalable AI that truly delivers at the edge.
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 ## What We Build 
 - **xLSTM architecture** — our next-generation core technology, developed under the leadership of AI pioneer Sepp Hochreiter, delivering higher efficiency compared to transformer models.
 [![Paper](https://img.shields.io/badge/arxiv-xlstm-red)](https://arxiv.org/abs/2405.04517)
 - **Foundation models for industrial data:** time-series, vision and multimodal.
 - **Edge-ready deployments**: compact, low-power, real-time inference across embedded and cloud-edge environments.
-- **Flagship model:** TiRex — a zero-shot forecasting foundation model (35 M parameters) built on xLSTM, tailored for time series in industrial settings.
-[![Paper](https://img.shields.io/badge/arxiv-tirex-red)](https://arxiv.org/abs/2505.23719)
-    [![PyPI](https://img.shields.io/pypi/v/tirex-ts?color=blue)](https://pypi.org/project/tirex-ts/)
-[![PyPI Downloads](https://static.pepy.tech/personalized-badge/tirex-ts?period=total&units=INTERNATIONAL_SYSTEM&left_color=GREY&right_color=GREEN&left_text=downloads)](https://pepy.tech/projects/tirex-ts)
+- **Flagship model:** TiRex — a zero-shot forecasting foundation model (35 M parameters) built on xLSTM, tailored for time series in industrial settings. [![Paper](https://img.shields.io/badge/arxiv-tirex-red)](https://arxiv.org/abs/2505.23719) [![PyPI](https://img.shields.io/pypi/v/tirex-ts?color=blue)](https://pypi.org/project/tirex-ts/) [![PyPI Downloads](https://static.pepy.tech/personalized-badge/tirex-ts?period=total&units=INTERNATIONAL_SYSTEM&left_color=GREY&right_color=GREEN&left_text=downloads)](https://pepy.tech/projects/tirex-ts)
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 ## Collaborate With Us 
 We welcome:
 - Industrial and enterprise partners looking to deploy efficient AI at scale