nomagic.ai valid
Nomagic
Nomagic is a warehouse robotics company applying breakthrough general-purpose Physical AI to automate picking, packing, sorting and induction for high throughput and reliability in complex fulfilment warehouses. Founded in 2017 and headquartered in Warsaw, Poland, its robots learn from millions of real-world tasks in 24/7 environments.
For agents: Warsaw-based warehouse robotics firm offering Physical AI robots for picking, packing, sorting and induction in fulfilment warehouses; solutions, technology and
warehouse roboticsphysical aiorder pickingfulfilment automationpocket sorter
- Authority
- 47 reach 6 · trust 5 · quality 21 · freshness 15
- Tier
- listed
- llms.txt
- https://nomagic.ai/llms.txt
- Sections
- 3
- Links
- 23
- Size
- 6778 bytes
- Popularity rank
- #747609
- Country
- United States
- Categories
- AI and machine learning
- For agents
- Documentation: developer or product docs worth reading, nothing to call
- Audience
- business · agents can: read docs · en
- First seen
- 2026-10-19T10:44:45.129Z
- Last checked
- 2026-10-21T14:38:00.631Z
- Badge
Their llms.txt
The beginning of the file, from our validated copy saved 2026-10-21T14:37:58.124Z. Cached copy · Source on nomagic.ai
# Nomagic
> Nomagic is a warehouse robotics company applying breakthrough general-purpose Physical AI to automate picking, packing, sorting and induction for high throughput and reliability in complex fulfilment warehouses. Founded in 2017 and headquartered in Warsaw, Poland, its robots learn from millions of real-world tasks in 24/7 environments.
Nomagic combines multi-layer AI, proprietary robotics hardware and real-time cloud data to power intelligent, autonomous fulfilment. Its AI modules, including Grip AI, Place AI and React AI, enable robots to identify objects, determine optimal grasp and placement, and react instantly to anomalies and changing inventory. A no-code, Robotics-as-a-Service deployment model integrates with existing WMS and WCS systems without disruption, and a standard API lets robot integrators add Nomagic's intelligence layer to their own hardware.
Trusted by leading retailers, e-commerce distributors and 3PLs including ASOS, Zalando, Arvato, Fiege, Komplett and Brack, Nomagic serves sectors such as fashion, general merchandise, pharmaceuticals and groceries. The company has raised over 84 million US dollars to scale deployments across Europe and the United States.
Last updated: 2026-10-21
## Overview
- [Home](https://nomagic.ai): Homepage covering Physical AI for end-to-end warehouse automation.
- [XML sitemap](https://nomagic.ai/sitemaps.xml): Complete index of all indexable pages.
- [Site search](https://nomagic.ai?s={query}): Keyword search across the site.
## Key pages
- [Library of Chaos](https://nomagic.ai/library-of-chaos/)
- [Pocket Sorter](https://nomagic.ai/solution/pocket-sorter/)
- [Technology](https://nomagic.ai/technology/)
- [Research Papers](https://nomagic.ai/research-papers/)
- [Meet us at MODEX 2026](https://nomagic.ai/meet-us-at-modex-2026/)
- [Solutions](https://nomagic.ai/solutions/)
- [Modex 2026](https://nomagic.ai/events/modex-2026/)
- [AI Roadmap](https://nomagic.ai/ai-roadmap/)
- [Nomagic University Lab](https://nomagic.ai/nomagic-university-lab/)
- [Innovation Hub](https://nomagic.ai/innovation-hub/)
## Optional
Recent news, insights and breakthroughs in Physical AI and warehouse automation. Lower priority and safe to skip under limited context.
- [Don't Wait for the Failure: Multiplying Deployment Data via Simulation](https://nomagic.ai/dont-wait-for-the-failure-multiplying-deployment-data-via-simulation/): By Szymon Trochimiak, Senior ML Research Engineer For physical AI, simulation and real-world are not competing but complementary data sources. Sometimes an item opens while the robot is carrying it. If the robot can see a disassembled item, it can stop and ask for help. That is the job of Live Anomaly Detection (LAD, for […]
- [The Last Few Percent: Getting Visual Order Verification Production-Ready (UPDATED)](https://nomagic.ai/the-last-few-percent-getting-visual-order-verification-production-ready/): By Michał Grzejdziak-Zdziarski, Senior Machine Learning Manager; Marku
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