Nomagic

A well-funded Polish warehouse robotics company with credible European deployments, vendor-only performance claims, and an unproven path to the United States.

---

How to Read This Report

This report applies a four-tier evidence grading system throughout. Every material assertion is labelled or contextualised according to the tier of its underlying source. Readers should weight conclusions accordingly.

Where the research dossier is thin on a topic, this report says so plainly rather than padding with inference dressed as fact. Bracketed numerals [n] refer to the numbered sources in §14.

---

01Executive Overview

Nomagic is a Warsaw-based warehouse robotics company that designs, manufactures, and deploys autonomous robotic systems for e-commerce and multichannel fulfilment operations. Founded in 2018, the company has built a portfolio of five distinct solutions — Pick, Pack, Sort, Spot, and the recently launched Shoebox Picker — and has accumulated a named customer list that includes ASOS, Zalando, Komplett Group, Cdiscount, Brack, and Lyko, among others, across six European countries ¹⁸¹⁰¹³.

As of January 2026, Nomagic has raised more than $84 million in total funding, the most recent tranche being a $10 million (approximately €8.3 million) Series B extension led by Cogito Capital Partners ⁸¹². The company is using that capital to pursue two objectives simultaneously: expanding into the United States market and advancing its development of visual-language-action (VLA) models, which it is positioning as the core of a next-generation "Physical AI" platform ⁸¹¹.

The commercial case for Nomagic is straightforward in outline. E-commerce fulfilment is labour-intensive, error-prone, and increasingly difficult to staff in European markets. Robotic picking and packing systems that can handle a broad range of SKUs with high accuracy and sustained uptime address a genuine operational problem. Nomagic's claim of 99.4% first-pick accuracy and 95%-plus SKU coverage, if accurate, would represent a commercially viable solution ¹². The difficulty is that all specific performance figures originate exclusively from Nomagic's own marketing materials. No independent operational audit, third-party benchmark, or named-customer data disclosure appears in the available evidence base. The figures are plausible for a mature system in controlled conditions; they are not verified.

Several structural observations frame the rest of this report. First, Nomagic is operating in a segment — autonomous piece-picking for e-commerce — that has historically been one of the hardest problems in warehouse robotics. The graveyard of well-funded companies that failed to achieve reliable unstructured-bin picking at commercial scale is long. Nomagic's deployment record across multiple named enterprise customers in live operations is therefore genuinely meaningful, even without independent performance data. Second, the company's pivot to "Physical AI" branding and VLA model development reflects an industry-wide shift in how warehouse robotics vendors are positioning their software layers, but the practical implications of that shift for Nomagic's near-term product performance are unclear. Third, the US expansion announced in January 2026 is a significant strategic bet. European fulfilment robotics and North American fulfilment robotics are not the same market in terms of customer expectations, competitive intensity, or integration standards.

This report examines each of these dimensions in turn, separating what the evidence supports from what Nomagic claims and what remains unknown.

Latest news

This module is being compiled — no data to show yet.

---

02The Nomagic Story

Origins and Founding Context

Nomagic was founded in Warsaw in 2018. The founding team's specific backgrounds are not detailed in the available evidence base — UNKNOWN — though the company's early positioning around AI-driven robotic manipulation suggests founders with backgrounds in computer vision, robotics, or machine learning. Warsaw's emergence as a technology hub, partly driven by the concentration of engineering talent from Polish universities and the presence of regional offices for major technology firms, provided a plausible talent pool for a deep-tech robotics startup.

The timing of the founding is relevant. By 2018, the e-commerce fulfilment robotics sector had already seen the first wave of investment, anchored by Amazon's 2012 acquisition of Kiva Systems (subsequently Amazon Robotics). The second wave — focused not on mobile shelving robots but on the harder problem of robotic piece-picking from unstructured bins — was just beginning to attract serious capital. Companies including Covariant, RightHand Robotics, Berkshire Grey, and Kindred were raising significant rounds in the 2017–2020 period. Nomagic entered this competitive field from a European base, which gave it a degree of geographic insulation from the most heavily capitalised US competitors but also constrained its access to the deepest pools of robotics and AI talent.

Funding History

The funding trajectory is one of the clearest verified data points available for Nomagic.

The $22 million round reported by TechCrunch in May 2022 described Nomagic as building "e-commerce warehouse picking robots" and noted the company had already deployed systems with customers including Brack ¹³. The subsequent $44 million round, reported by The Robot Report, was described as funding to "scale European picking robot deployments," with EBRD Venture Capital confirmed as a participant ¹⁰. The January 2026 Series B extension of $10 million, led by Cogito Capital Partners, was explicitly tied to two strategic objectives: US market expansion and VLA model development ⁸¹².

The total of more than $84 million places Nomagic in the upper tier of European warehouse robotics startups by capital raised, though it remains substantially below the capital bases of the largest US competitors. The involvement of EBRD Venture Capital — the venture arm of the European Bank for Reconstruction and Development — is notable as a signal of institutional confidence in the company's regional significance, though EBRD's mandate includes supporting Central and Eastern European technology development, which may have influenced the investment thesis beyond pure commercial return expectations.

Strategic Narrative Evolution

The language Nomagic uses to describe itself has shifted materially over its history. In 2022, TechCrunch described the company in straightforward terms as building "e-commerce warehouse picking robots" ¹³. By January 2026, the company's own press release was describing its offering as "Physical AI for end-to-end warehouse automation" and positioning its VLA model development as a core technology differentiator ⁸. The company's website homepage now leads with "Physical AI for End-to-End Warehouse Automation" ¹.

This shift from "picking robots" to "Physical AI" mirrors a broader industry trend. The term "Physical AI" — popularised in part by NVIDIA's Jensen Huang in his 2025 CES keynote — has been adopted by a wide range of robotics vendors as a way of signalling that their systems are driven by foundation-model-scale AI rather than traditional rule-based or narrow-ML approaches. Whether Nomagic's use of the term reflects a genuine architectural shift in its technology stack or is primarily a repositioning of existing capabilities under a more fashionable label is a question the available evidence does not resolve. EDITORIAL INFERENCE: the timing of the language shift, coinciding with a fundraising announcement and a US expansion push, suggests at minimum a deliberate marketing decision; the degree to which it reflects a substantive technical transition is unclear.

Geographic Footprint

Nomagic's operational footprint is concentrated in Europe, with confirmed or named deployments in Switzerland (Brack/Brack.Alltron), Norway (Komplett Group), Sweden (Lyko), France (Cdiscount), Germany (Arvato, Fiege, apo.com), and the United Kingdom (ASOS) ¹⁸¹⁰¹³. The company's headquarters remain in Warsaw ⁸¹². US expansion is stated as a strategic priority for 2026 and beyond, funded in part by the January 2026 extension round ⁸¹², but no US customer deployments are confirmed in the available evidence base.

---

03Product Portfolio: What Nomagic Actually Sells

Nomagic's commercial portfolio comprises five named solutions. The evidence base for each varies in depth; the core Pick and Pack solutions have the most corroboration from independent sources, while Spot and the Shoebox Picker are more recent and rely more heavily on official sources.

Pick

The Pick solution is Nomagic's foundational product and the one with the longest deployment history ²¹³. It is described as an AI-powered robotic picking system capable of identifying and grasping items from unordered bins or totes across a claimed range of millions of SKUs ². The system uses a robotic arm equipped with specialist grippers — including a universal gripper — to handle items of varying size, shape, weight, and packaging type ²¹³.

COMPANY CLAIM: 99.4% first-pick accuracy and 95%-plus SKU coverage ¹². These figures are vendor-stated and not independently verified.

COMPANY CLAIM: The system operates for up to 12 hours with limited human intervention ¹. "Limited" is undefined in the available sources.

VERIFIED: The system has been deployed with named enterprise customers including Brack (confirmed by TechCrunch ¹³), Komplett Group, and ASOS ¹¹⁰.

VERIFIED: The system integrates with AutoStore, shuttle ports, SAP, and conveyor systems ¹².

The Pick solution addresses the hardest sub-problem in warehouse robotics: unstructured bin-picking of heterogeneous consumer goods. The fact that Nomagic has live deployments with major e-commerce operators is meaningful evidence that the system clears a minimum commercial viability threshold. The specific accuracy figures remain unverified.

Pack

The Pack solution automates the packing process — placing picked items into appropriate shipping containers — and is described as working in conjunction with the Pick solution or as a standalone deployment ³. It includes an Item Pusher component and is designed to handle the variability in item dimensions that makes automated packing technically challenging ³.

COMPANY CLAIM: 99.8% scanning accuracy ¹³. Vendor-stated; not independently verified.

VERIFIED: Pack is listed as a deployed solution on the official website and is referenced in customer case studies ¹³.

UNKNOWN: Throughput rates, cycle times, and the range of packaging formats supported are not specified in the available evidence base.

Sort

The Sort solution uses a component described as the "justInduct arm" for high-speed sorting operations, designed to integrate with existing conveyor and sortation infrastructure ⁴. It is positioned for applications where items need to be routed to specific destinations — by order, by carrier, by zone — at high throughput ⁴.

VERIFIED: Sort is listed as an active product on the official website ⁴.

UNKNOWN: Throughput rates (items per hour), error rates, and the range of sortation topologies supported are not specified in the available evidence base.

Spot

Spot is described on the official website as a solution within the Nomagic portfolio ¹. Beyond its listing, the available evidence base contains limited detail on Spot's specific function, hardware configuration, or deployment status. UNKNOWN: the precise operational role of Spot — whether it is an inspection system, a mobile platform, or something else — is not clearly described in the sources available to this report.

Shoebox Picker

The Shoebox Picker is the most recently announced product in the portfolio and the one generating the most current attention. It is described as a system specifically designed to handle two-piece shoeboxes — the lid-and-base format common in footwear retail — which present particular challenges for robotic manipulation due to their size, weight distribution, and the need to handle them without damaging the box or its contents ¹⁸.

COMPANY CLAIM: "The first Physical AI capable of handling delicate two-piece shoeboxes" ¹⁸. The "first" designation is a vendor assertion; it has not been independently verified, though IFOY Award 2026 finalist status for Innovation of the Year provides partial independent validation of the product's novelty ¹.

VERIFIED: IFOY (International Forklift of the Year) Award 2026 finalist status in the Innovation of the Year category is confirmed ¹. IFOY is an independent industry award body with an established evaluation process, which lends some credibility to the novelty claim without confirming the "first" assertion.

EDITORIAL INFERENCE: The Shoebox Picker represents a deliberate vertical specialisation strategy. Rather than competing solely on general-purpose picking capability, Nomagic is developing application-specific variants that address known pain points in particular retail verticals. Footwear is a large e-commerce category with distinctive fulfilment challenges; a credible shoebox-handling solution would have clear commercial value to footwear retailers and 3PLs serving them.

Portfolio Summary

Products & versions

Nomagic Pick

AI-powered robotic picking arm that autonomously identifies and picks items from unordered bins, claiming 99.4% first-pick accuracy and 95%+ SKU coverage for e-commerce fulfillment.

Nomagic Pack

Automated robotic packing solution that handles the packing stage of warehouse fulfillment, operating up to 12 hours with limited human intervention as part of Nomagic's end-to-end warehouse automation suite.

Nomagic Sort

High-speed robotic sorting system featuring the justInduct arm and Item Pusher component, designed for multichannel fulfillment sorting operations in e-commerce and 3PL warehouses.

Nomagic Spot

A warehouse robotics solution listed among Nomagic's core product lineup for end-to-end warehouse automation, targeting e-commerce and multichannel fulfillment environments.

Nomagic Shoebox Picker

A specialist robotic picker for delicate two-piece shoeboxes, claimed by Nomagic as the first Physical AI solution for this use case; named an IFOY Award 2026 finalist for Innovation of the Year.

---

04Technology Stack: Strengths and the Work That Remains

The AI Brain

Nomagic describes its core software layer as the "AI Brain" — a system that it claims enables autonomous problem-solving when the robot encounters unexpected situations during operation ¹². The AI Brain is described as having been trained on "billions of product configurations and millions of real operational tasks" ¹⁸. This framing positions the system as learning from operational data at scale, which is a credible approach to improving robustness in unstructured environments.

COMPANY CLAIM: The AI Brain "autonomously problem-solves complexity and the unexpected" ¹. This is a vendor assertion. The specific mechanisms — whether this means the system retries with alternative grasp strategies, escalates to a human exception handler, or genuinely resolves novel situations without human input — are not described in sufficient technical detail in the available sources to evaluate independently.

EDITORIAL INFERENCE: The claim that the system has been trained on "billions of product configurations" is consistent with the scale of data that a company with multiple years of live deployments across major e-commerce operators could plausibly accumulate. ASOS alone carries several hundred thousand active SKUs; Zalando carries millions. If Nomagic's systems have been running in live operations at these customers for multiple years, the training data claim is at least plausible in order of magnitude. Whether the training methodology translates to genuine generalisation or primarily to interpolation within a known distribution is a technical question the available evidence does not answer.

Visual-Language-Action Models

The January 2026 funding announcement explicitly cited VLA model development as a strategic technology investment ⁸¹¹¹². VLA models — which combine visual perception, natural language understanding, and action generation in a single model architecture — represent the current frontier of research in robotic manipulation. The approach, exemplified by systems such as Google DeepMind's RT-2 and subsequent work, aims to produce robots that can follow natural language instructions and generalise to novel objects and tasks without task-specific programming.

COMPANY CLAIM: Nomagic is developing VLA models that "auto-integrate into robot fleet" ⁸. The meaning of "auto-integrate" is not technically specified in the available sources.

EDITORIAL INFERENCE: The VLA development announcement is strategically significant for two reasons. First, it signals that Nomagic is investing in the software layer that will determine long-term competitive differentiation, rather than competing purely on hardware or systems integration. Second, it raises a legitimate question about the relationship between the VLA development roadmap and the current deployed product. VLA models at the scale required for robust generalisation in warehouse environments are computationally expensive to train and inference, and the gap between research-grade VLA demonstrations and production-grade warehouse deployment is substantial. The available evidence does not clarify whether Nomagic's VLA work is already embedded in deployed systems or is a forward-looking R&D investment.

Gripper Technology

Nomagic's hardware includes multiple specialist grippers, including a universal gripper, designed to handle the range of item types encountered in e-commerce fulfilment ²¹³. Gripper design is a critical bottleneck in piece-picking robotics; the ability to reliably grasp items ranging from soft polybags to rigid boxes to irregular shapes is one of the primary technical challenges the field has struggled with.

VERIFIED: The use of multiple specialist grippers, including a universal gripper, is confirmed by both official sources and TechCrunch's independent reporting ²¹³.

UNKNOWN: The specific gripper designs, the switching mechanism between gripper types (if any), and the performance characteristics of each gripper across different item categories are not publicly disclosed.

System Integration

Nomagic's solutions are described as integrating with AutoStore, shuttle ports, SAP, and conveyor systems ¹². This integration breadth is commercially important: most European e-commerce fulfilment centres already have significant installed infrastructure, and a picking solution that requires wholesale replacement of existing systems faces a much higher adoption barrier than one that can be bolted onto existing AutoStore or conveyor installations.

VERIFIED: Integration with AutoStore, shuttle ports, SAP, and conveyor systems is stated in official product documentation ¹².

UNKNOWN: The depth and reliability of these integrations — whether they are certified by the respective platform vendors, whether they have been deployed in live customer environments, and what the implementation timeline looks like — is not specified in the available evidence.

24/7 Monitoring and Support

All Nomagic solutions are described as including 24/7 monitoring and support ¹. This is a standard feature for enterprise warehouse robotics deployments, where unplanned downtime has direct revenue impact.

COMPANY CLAIM: 24/7 monitoring and support included with all solutions ¹. The operational model — whether this is remote monitoring, on-site support, or a combination — is not detailed.

Strengths and Gaps: An Editorial Assessment

The most significant gap in the technology evidence base is the complete absence of independent technical validation. No peer-reviewed papers, no third-party benchmarks, no customer-disclosed operational data, and no independent teardown or audit appear in the available sources. This is not unusual for a commercial warehouse robotics company — most do not publish technical papers — but it means that all specific performance claims must be treated as vendor assertions until independently verified.

---

05Research, Papers, Authors and Labs

The research dossier for this report contains zero entries in the research category (count: 0). This is a significant gap and warrants direct acknowledgement.

UNKNOWN: No peer-reviewed publications authored or co-authored by Nomagic researchers have been identified in the available evidence base. No named researchers or principal scientists are identified in the dossier sources. No academic or institutional laboratory affiliations are disclosed in the available sources. No public code repositories or datasets associated with Nomagic's core technology have been identified.

This absence does not necessarily indicate that Nomagic conducts no research or employs no researchers with academic backgrounds. Commercial warehouse robotics companies frequently conduct substantial internal R&D without publishing externally, for competitive reasons. However, it does mean that this report cannot assess the technical depth or originality of Nomagic's AI and robotics research through the standard lens of academic output.

The January 2026 funding announcement's reference to VLA model development ⁸ implies that Nomagic has or is building a research capability in this area. VLA model development at a meaningful scale typically requires researchers with backgrounds in large-scale machine learning, computer vision, and robotic manipulation — a talent profile that is scarce globally and particularly scarce in Central and Eastern Europe. Whether Nomagic is building this capability internally, partnering with academic institutions, or licensing technology from third parties is not disclosed in the available evidence.

The IFOY Award 2026 finalist status for the Shoebox Picker ¹ provides the closest thing to independent technical validation available in the evidence base, but IFOY evaluations are industry awards rather than peer-reviewed technical assessments.

Company-linked papers

This module is being compiled — no data to show yet.

Authors & labs

This module is being compiled — no data to show yet.

Code & simulation

This module is being compiled — no data to show yet.

Datasets & benchmarks

This module is being compiled — no data to show yet.

---

06Media Evidence Library: What the Videos Prove

The research dossier for this report contains zero entries in the video category (count: 0). This is a notable absence for a company that, like all warehouse robotics vendors, almost certainly maintains a library of demonstration and deployment videos on its website and YouTube channel.

The absence of video evidence in the dossier means this report cannot apply the standard analytical framework for evaluating robotic demonstration footage — specifically, the discipline of distinguishing between what a choreographed demo video proves (that the system can perform a task under controlled conditions at least once) and what it does not prove (sustained autonomous operation, performance at rated throughput, handling of edge cases, or real-world deployment conditions).

EDITORIAL INFERENCE: Nomagic's official website and press materials almost certainly include video content showing its systems operating in warehouse environments. The absence of this content from the dossier reflects the evidence-gathering methodology rather than the non-existence of such content. Any video evidence that exists should be evaluated with the following questions in mind:

1. Is the environment clearly a live customer deployment, or a controlled demonstration cell?
2. Is the item range shown representative of the claimed SKU coverage, or a curated subset of easy-to-handle items?
3. Is the throughput shown consistent with the claimed operational performance?
4. Is there evidence of human intervention during the filmed sequence?
5. Are failure cases or recovery behaviours shown, or only successful picks?

The TechCrunch article from May 2022 describes the system as one that "identifies and picks items from unordered bins" and includes reporting from what appears to be a customer deployment context ¹³. The Robot Report's coverage of the $44 million round similarly describes the system in operational terms ¹⁰. These independent journalistic descriptions, while not video evidence, provide some corroboration that the system performs its described function in real environments.

Media library

This module is being compiled — no data to show yet.

---

07Commercial Reality

Customer Base: What Is Verified

The most commercially significant verified fact about Nomagic is that it has a named customer list of major European e-commerce and 3PL operators. The following table summarises the evidence basis for each named customer.

The distinction between a logo appearing on a vendor's website and a confirmed, active, productive deployment is important. Logo lists are a standard marketing practice and typically reflect some form of commercial relationship, but they do not specify the scope, duration, or current status of that relationship. The strongest evidence in this list is for Brack (independent confirmation by TechCrunch in 2022 ¹³) and Komplett Group (named in official case studies ¹).

EDITORIAL INFERENCE: The breadth and quality of the customer list — spanning multiple European countries, multiple retail verticals, and including both direct e-commerce operators and 3PLs — is consistent with a company that has achieved genuine commercial traction. A startup that had failed to deliver operationally would not retain major customers like ASOS or Zalando on its reference list. The presence of 3PL operators (Arvato, Fiege) is particularly notable, as 3PLs operate across multiple client environments and have strong incentives to remove underperforming technology.

Revenue and Financial Performance

UNKNOWN: Nomagic is a private company and does not disclose revenue figures. No revenue estimates from independent analysts appear in the available evidence base. The company's total funding of more than $84 million ⁸ and its multi-year deployment history with major customers suggests it has achieved meaningful revenue, but the specific figures are not available.

Deployment Model and Commercial Terms

UNKNOWN: Nomagic's commercial model — whether it sells hardware outright, operates on a robotics-as-a-service (RaaS) subscription model, or uses a hybrid approach — is not specified in the available evidence base. This is a commercially significant unknown. RaaS models, which have become common in warehouse robotics, shift capital expenditure to operating expenditure for customers and create recurring revenue streams for vendors, but they also require vendors to carry the capital cost of deployed hardware on their balance sheets.

The "104 Additional Days of Productivity" Claim

Nomagic's marketing materials include a claim that its systems deliver "up to 104 additional days of productivity per year" compared to manual operations ¹. This figure presumably derives from a calculation based on the system's ability to operate during hours when human workers are not present — nights, weekends, and holidays.

COMPANY CLAIM: Up to 104 additional days of productivity per year ¹. This is a vendor-stated figure with no disclosed methodology.

EDITORIAL INFERENCE: The arithmetic is plausible in principle. A system that operates two additional shifts per day (16 hours) on weekends and holidays could theoretically generate a figure in this range. However, the claim assumes the system operates at full productive capacity during those additional hours, which requires that inbound inventory is available, that the system does not require maintenance or intervention, and that outbound logistics can absorb the additional throughput. In practice, the achievable uplift will depend heavily on the specific operational context of each deployment.

US Expansion: Ambition Versus Evidence

The January 2026 Series B extension was explicitly framed as funding for US market expansion ⁸¹². This is a significant strategic move. The US e-commerce fulfilment robotics market is larger than the European market in aggregate, but it is also more competitive, with established players including Symbotic, Berkshire Grey (now part of SoftBank Robotics), Covariant, RightHand Robotics, and Amazon Robotics itself.

VERIFIED: US expansion is a stated strategic objective, funded by the January 2026 round ⁸¹².

UNKNOWN: No US customer deployments, partnerships, or pilot agreements are confirmed in the available evidence base as of the coverage date of this report.

EDITORIAL INFERENCE: The gap between announcing a US expansion and achieving productive US deployments is substantial. It involves establishing a local sales and support infrastructure, navigating different integration standards and customer expectations, and competing against US-based vendors with established customer relationships and, in some cases, significantly larger capital bases. Nomagic's European deployment record is a genuine asset in these conversations, but it does not automatically translate to US commercial success. The $10 million extension round is relatively modest capital for a US market entry of meaningful scale.

Claim-Versus-Evidence Summary for Commercial Section

Customers & deployments

ASOSFashion E-commerce

Named customer of Nomagic, listed on the official Nomagic website as a deployed customer for warehouse automation.

ZalandoFashion E-commerce

Named customer of Nomagic, confirmed by The AI Insider as a deployment customer for Nomagic's warehouse robotics.

Komplett GroupConsumer Electronics E-commerce

Named customer featured in official Nomagic case studies, deploying Nomagic warehouse robotics in Norway.

Brack / Brack.AlltronConsumer Electronics E-commerce

Named customer confirmed by TechCrunch and featured in official Nomagic case studies, deploying Nomagic robotics in Switzerland.

CdiscountGeneral E-commerce

Named customer of Nomagic, listed on the official Nomagic website as a deployed customer for warehouse automation in France.

LykoBeauty & Health E-commerce

Named customer of Nomagic, listed on the official Nomagic website as a deployed customer for warehouse automation.

apo.comOnline Pharmacy

Named customer of Nomagic, listed on the official Nomagic website as a deployed customer for warehouse automation.

ArvatoThird-Party Logistics (3PL)

Named customer of Nomagic, listed on the official Nomagic website as a deployed 3PL customer for warehouse automation.

FiegeThird-Party Logistics (3PL)

Named customer of Nomagic, listed on the official Nomagic website as a deployed 3PL customer for warehouse automation.

Vetlog.oneVeterinary / Pet Supplies E-commerce

Named customer of Nomagic, listed on the official Nomagic website as a deployed customer for warehouse automation.

08Markets and Use Cases

Nomagic's commercial footprint maps onto a narrow but high-value slice of the logistics automation market: the goods-to-person and person-to-goods picking, packing, and sorting workflows that sit at the operational heart of e-commerce and multichannel fulfilment. The company has deliberately avoided the broader warehouse automation market — autonomous mobile robots, conveyor infrastructure, warehouse management software — and instead concentrated on the manipulation layer: the moment when a robot must identify, grasp, and correctly handle an individual item from a heterogeneous bin or tote.

That focus is commercially rational. Manipulation remains the hardest unsolved problem in warehouse robotics, and the gap between what a human picker can handle and what a robot can reliably grasp has historically been the primary barrier to automation in fashion, footwear, and general merchandise. Nomagic's claimed 95-plus percent SKU coverage and 99.4 percent first-pick accuracy, if independently validated, would represent a commercially meaningful threshold for operators whose SKU catalogues run into the hundreds of thousands ¹².

E-commerce and fashion fulfilment is the clearest primary market. ASOS and Zalando — both named customers ¹ — operate among the most demanding picking environments in European logistics: high SKU counts, irregular item geometries, seasonal volume spikes, and zero tolerance for mispicks that trigger returns. The ability to handle soft goods, polybags, and irregular packaging without dedicated fixturing is the capability that differentiates Nomagic from earlier-generation bin-picking systems that required structured presentation.

Footwear is an emerging vertical, evidenced by the Shoebox Picker product and its IFOY Award 2026 finalist status ⁸¹¹. Two-piece shoeboxes present a specific manipulation challenge — the lid must be removed, the item identified, and the box reassembled or discarded — that generic picking arms have historically struggled with. Whether Nomagic's claim to be the first Physical AI system capable of handling this workflow is accurate is unverified ⁸, but the IFOY nomination provides at least partial independent acknowledgement that the capability is novel.

Third-party logistics (3PL) operators represent a structurally attractive segment. Named customers Arvato and Fiege ¹ are large European 3PLs that operate fulfilment on behalf of multiple brand clients. For a 3PL, the ability to deploy a single robotic platform across diverse client SKU catalogues — without retraining or re-fixturing for each client — is the critical value proposition. Nomagic's claimed breadth of SKU coverage is directly relevant here, though the absence of independent verification means 3PL buyers must conduct their own pilots.

Consumer electronics and pharmacy are represented by Komplett Group (Norway) and apo.com (Germany) ¹¹⁰. These verticals introduce different handling requirements: fragile items, regulated products, and in the pharmacy case, potential compliance obligations around pick verification. Nomagic's 99.8 percent scanning accuracy claim ¹ is particularly relevant to pharmacy, where mispick consequences extend beyond customer dissatisfaction to patient safety. That figure is unverified by independent audit.

Grocery and general merchandise are referenced in official materials as target verticals ¹ but no named grocery customer appears in the evidence base. This is a notable gap: grocery picking involves fresh produce, variable weights, and crush-sensitive items that represent a harder manipulation problem than fashion or electronics. The absence of a named grocery customer suggests either that Nomagic has not yet penetrated this vertical or that any deployments are under non-disclosure.

The geographic market is currently European, with deployments confirmed or implied in Switzerland (Brack) ¹³, Norway (Komplett) ¹⁰, Sweden (Lyko) ¹, Denmark, France (Cdiscount) ¹, and Germany (apo.com, Zalando) ¹. The January 2026 Series B extension explicitly targets US expansion ⁸¹², which would represent a significant step-change in commercial scale and competitive exposure. The US market features incumbent players with deeper capital reserves and longer customer relationships; the conditions under which Nomagic's European track record translates to US contract wins are not yet established.

The market sizing context is relevant for investors and buyers. The global warehouse automation market is large and growing, with manipulation robotics representing a fraction of total spend but the fastest-growing sub-segment as labour costs rise across European and North American logistics. Nomagic is competing for a share of the automation capital expenditure that operators would otherwise direct toward additional headcount or simpler fixed automation. The company's positioning as a Physical AI platform — rather than a single-task robot — is an attempt to capture a larger share of that budget by addressing multiple workflow steps with a common AI layer.

09Competitive Landscape

Nomagic operates in a market segment that has attracted substantial capital and several well-resourced competitors. The competitive analysis below is based on publicly available information; it does not constitute a comprehensive market survey, and relative performance claims from any vendor in this space should be treated with the same scepticism applied to Nomagic's own figures.

The competitive field can be divided into three groups: established US-origin players with significant deployment scale, European peers at comparable or earlier stages, and the in-house automation programmes of large logistics operators.

Established US-origin players

Berkshire Grey (now part of SoftBank Robotics' broader portfolio following acquisition activity) and Symbotic have both deployed robotic picking and sorting at scale in North American retail and grocery distribution. Symbotic in particular has disclosed revenue and customer contracts at a scale that dwarfs Nomagic's current footprint. However, these systems are typically designed for greenfield or heavily retrofitted facilities and carry higher integration costs and longer deployment timelines than Nomagic claims for its solutions.

Covariant (now integrated into ABB Robotics following ABB's 2024 acquisition) brought a foundation-model approach to bin picking that is conceptually similar to Nomagic's AI Brain framing. The ABB acquisition gives Covariant's technology access to ABB's global sales and integration network — a distribution advantage that Nomagic cannot match at its current scale.

Mujin and Righthand Robotics (acquired by Stow Group) represent the more established tier of piece-picking specialists. Both have multi-year deployment histories and customer references in e-commerce and 3PL. Righthand Robotics' integration into Stow Group's broader intralogistics offering mirrors the bundled-solution approach that Nomagic is attempting with its multi-product portfolio.

European peers

Magazino (Germany) focuses on shelf-picking for e-commerce and has deployed in European 3PL environments. Pickle Robot (US-origin but active in European markets) targets depalletising and trailer unloading rather than piece-picking, so the overlap is partial. Dorabot and Geek+ (both China-origin) have European sales presences and offer sorting and picking solutions that compete on price.

The most direct European competitor in the fashion and e-commerce picking segment is arguably Ocado Technology's robotic picking arm programme, though Ocado's technology is primarily deployed within its own fulfilment network rather than sold as a standalone product to third parties.

In-house programmes

Amazon Robotics represents the ceiling of what vertically integrated automation can achieve, but Amazon's technology is not available to third-party operators. The existence of Amazon's programme does, however, set the performance benchmark that external vendors are measured against and accelerates the pace at which large operators expect robotic picking to improve.

Nomagic's competitive positioning rests on three claimed advantages: breadth of SKU coverage without retraining, a multi-solution portfolio (Pick, Pack, Sort, Spot, Shoebox Picker) that addresses more of the fulfilment workflow than single-task competitors, and a Physical AI framing that positions the AI layer as the durable asset rather than the hardware. Whether these advantages are durable depends on how quickly larger competitors with more capital can close the gap on SKU coverage and manipulation dexterity — a question the current evidence base cannot answer.

The US expansion announced in January 2026 ⁸¹² will expose Nomagic to the full competitive intensity of the North American market, where several of the above competitors have home-field advantages in customer relationships, regulatory familiarity, and service infrastructure. The conditions under which a Warsaw-headquartered company with a European customer base wins US enterprise contracts against incumbents with US operations are not yet established.

Competitive comparison

Robot	Maker	Autonomy	Conf.
iRobot Roomba Combo 10 Max	iRobot	Autonomous	0.90
Mobile ALOHA (Stanford)	Stanford University	Teleoperated	0.90
1X NEO	1X Technologies	Remote-Assisted	0.90

10Geopolitical Context and Constraints

Nomagic's situation is shaped by several geopolitical and structural factors that are worth examining separately from the commercial analysis.

Poland as a robotics base

Warsaw has developed a credible engineering talent pool, partly as a consequence of Poland's strong mathematics and computer science university tradition and partly because of the migration of technical talent from neighbouring countries following the 2022 Russian invasion of Ukraine. Nomagic's ability to recruit AI and robotics engineers in Warsaw at costs below those in London, Munich, or San Francisco is a structural cost advantage that partially offsets the company's smaller capital base relative to US competitors ¹²¹³.

Poland's membership of the European Union provides access to EU single-market customers without customs friction and makes Nomagic eligible for EU-funded research and innovation programmes. The EBRD Venture Capital participation in an earlier funding round ¹⁰ reflects the European development finance community's interest in supporting technology companies in Central and Eastern Europe. This is a modest but real structural advantage for fundraising.

European regulatory environment

The EU AI Act, which entered into force in 2024 and is being phased in through 2026 and 2027, classifies certain AI systems used in safety-critical or high-impact environments under its high-risk category. Warehouse robotics operating alongside human workers may attract obligations around transparency, human oversight, and conformity assessment. Nomagic's "limited human intervention" framing and its AI Brain's autonomous problem-solving claims will need to be assessed against these requirements as the Act's provisions take effect. The company has not publicly disclosed its EU AI Act compliance posture, and this is an unknown that buyers in regulated sectors should investigate.

The EU Machinery Regulation (replacing the Machinery Directive) introduces updated safety requirements for collaborative and autonomous industrial machinery. Nomagic's hardware must comply with CE marking requirements and relevant ISO/EN standards for industrial robot safety. The evidence base does not include details of Nomagic's safety certifications beyond general commercial deployment confirmation.

US expansion and ITAR/export considerations

Nomagic's technology — AI-driven robotic manipulation — does not obviously fall within ITAR or EAR dual-use export control categories as currently defined, but the evolving US regulatory posture on AI and robotics technology from non-allied-but-non-US origins warrants monitoring. Poland is a NATO member and EU member state, which substantially reduces the political risk of US market entry compared to, for example, a Chinese-origin competitor. However, US federal procurement and defence-adjacent logistics contracts may impose domestic content or security review requirements that Nomagic would need to navigate.

Supply chain and hardware sourcing

Nomagic's robotic arms and gripper systems incorporate components sourced from global supply chains. The evidence base does not disclose Nomagic's hardware supply chain in detail, so the extent of exposure to semiconductor shortages, East Asian component dependencies, or tariff changes affecting robotic hardware imports into the EU or US is unknown. This is a material unknown for buyers considering long-term deployment commitments.

Labour market dynamics

The primary demand driver for warehouse robotics in Europe is the tightening of labour markets in logistics, driven by demographic trends, post-Brexit labour mobility changes in the UK, and rising minimum wages across EU member states. These trends structurally favour Nomagic's addressable market. Conversely, any significant reversal — such as increased migration flows or economic contraction reducing e-commerce volumes — would reduce the urgency of automation investment among potential customers.

The Russia-Ukraine conflict has had a secondary effect on European logistics networks, accelerating nearshoring and reshoring of fulfilment operations in Central and Eastern Europe. This creates potential demand for automation in new or expanded fulfilment centres in Poland and neighbouring markets, which Nomagic is geographically well-positioned to serve.

11The Hype, the Real and the Ugly

This section applies systematic scepticism to Nomagic's public claims, separating what the evidence supports from what requires independent verification and what is straightforwardly unverified marketing.

What the evidence supports

Nomagic is a real company with real customers. The presence of ASOS, Zalando, Komplett, Brack, Cdiscount, Arvato, and Fiege as named customers ¹¹⁰¹³ — with Brack independently confirmed by TechCrunch ¹³ and Komplett referenced in official case studies ¹ — establishes that the company has moved beyond pilot deployments into operational relationships with recognisable enterprise operators. This is a meaningful threshold that many robotics startups do not reach.

The funding trajectory is real and independently corroborated: $22M in 2022 ¹³, a subsequent $44M round ¹⁰, and a $10M Series B extension in January 2026 ⁸¹¹¹², totalling over $84M. The Series B extension was led by Cogito Capital Partners with EBRD Venture Capital participation ⁸¹⁰, both of which are identifiable institutional investors.

The IFOY Award 2026 finalist status for the Shoebox Picker ⁸¹¹ provides partial independent validation that the product represents a genuine technical contribution, since IFOY nominations involve assessment by an independent jury of logistics professionals. This is not proof of commercial performance but it is evidence of technical credibility.

The multi-product portfolio — Pick, Pack, Sort, Spot, Shoebox Picker — and the named system integrations with AutoStore, SAP, and conveyor systems ¹ are consistent with a company that has been through multiple customer deployment cycles and has had to build integration capability to survive.

What requires independent verification

The 99.4 percent first-pick accuracy and 99.8 percent scanning accuracy figures ¹² are vendor-only claims with no independent corroboration in the evidence base. These figures are plausible for a mature system operating on a representative subset of SKUs, but "first-pick accuracy" is a metric that can be defined in ways that flatter performance — for example, by excluding items the system declines to attempt, or by measuring only on SKUs within the system's trained distribution. Without a published methodology and independent audit, these numbers cannot be used as a basis for procurement decisions.

The "95-plus percent SKU coverage" claim ¹ has the same problem. Coverage is meaningful only relative to a defined SKU universe. A system that covers 95 percent of a fashion retailer's core catalogue but declines all irregular or oversized items may report 95 percent coverage while leaving a significant operational tail for human pickers. The evidence base does not disclose how coverage is measured.

The "up to 12 hours with limited human intervention" operational claim ¹ is ambiguous on two dimensions: what counts as "limited" intervention, and whether 12 hours is a typical or maximum figure. A system that requires a human to clear a jam every 45 minutes might still be described as operating with "limited" intervention. Independent operational data from a named customer would substantially change the confidence level on this claim.

The VLA (visual-language-action) model development referenced in the Series B extension announcement ⁸¹¹ is described in terms consistent with current AI research directions, but the claim that these models are "trained on billions of product configurations and millions of real operational tasks" ¹ is unverified. The scale of training data claimed is large; whether it reflects actual data collected from operational deployments or is a marketing extrapolation is unknown.

What is straightforwardly problematic

The claim that Nomagic's Shoebox Picker is "the first Physical AI capable of handling delicate two-piece shoeboxes" ⁸ is a vendor assertion that has not been independently verified. IFOY finalist status does not confirm the "first" designation. Competitors including Righthand Robotics, Mujin, and others have published work on handling irregular packaging; whether any of them have addressed two-piece shoeboxes specifically is not established in the evidence base, but the "first" claim should be treated with caution.

The "Physical AI" branding ¹ is a marketing term, not a technical classification. It appears to be Nomagic's adoption of a framing popularised by NVIDIA and others to describe embodied AI systems. The term does not carry a defined technical meaning and its use does not constitute evidence of any specific capability.

The absence of any independent operational audit, third-party benchmark, or published customer case study with quantitative performance data is the most significant gap in the evidence base. Every specific performance figure in Nomagic's public materials originates from Nomagic. This is not unusual for a company at this stage, but it means that buyers and investors are making decisions based entirely on vendor-supplied data.

Claim tracker

Nomagic robots achieve 99.4% first-pick accuracy and 99.8% scanning accuracyNot supported

These figures appear only in vendor/official sources [1][2]; no independent third-party benchmark, customer audit, or operational report in the dossier corroborates either metric — they must be treated as unverified marketing claims.

Nomagic robots operate autonomously for up to 12 hours with limited human intervention, delivering up to 104 additional days of productivity per yearNot supported

Both the 12-hour runtime and 104-day productivity gain are vendor-only claims [1][8]; independent sources (TechCrunch [13], The Robot Report [10]) describe the system as autonomous but do not measure or verify intervention rates or productivity uplift figures.

Nomagic robots cover 95%+ of SKUs for picking and packing across millions of product configurationsNot supported

The 95%+ SKU coverage claim originates exclusively from official/vendor sources [1][2][3]; no independent customer report, third-party test, or operational audit in the dossier confirms this coverage figure in real-world deployments.

Nomagic robots are deployed in live commercial operations with named enterprise customers including ASOS, Zalando, Komplett, and Brack across EuropeSupported

TechCrunch [13] independently confirms Brack as a customer; The Robot Report [10] corroborates European commercial deployments; The AI Insider [11] names Zalando — though deployment scale, unit counts, and operational outcomes at each site remain unverified by independent audit.

Nomagic has raised over $84M in total funding and is planning US market expansionSupported

Multiple independent sources — The Robot Report [10], TechCrunch [13], EU Startups [12], and The AI Insider [11] — corroborate individual funding rounds totalling >$84M and confirm US expansion as a stated use of the January 2026 Series B extension proceeds; actual US deployment remains unconfirmed.

12Future Scenarios

The following scenarios are editorial inferences based on the available evidence. They are not forecasts and should not be treated as such.

Scenario A: Controlled European consolidation (base case)

Nomagic continues to deepen its European customer base, adding two to four new enterprise accounts per year across its existing verticals. The US expansion announced in January 2026 ⁸¹² proceeds cautiously, with one or two pilot deployments in the first 18 months rather than a rapid land-grab. VLA model development progresses but does not produce a step-change in SKU coverage within the near term. The company reaches cash-flow neutrality on its European operations before requiring a Series C, using the $10M extension to fund the US sales infrastructure.

This scenario is the most consistent with the evidence: a company with a real but modest customer base, a credible technology, and a funding trajectory that suggests investors are satisfied with incremental progress rather than expecting hypergrowth. The risk in this scenario is that larger competitors with more capital close the performance gap and commoditise the picking layer before Nomagic can establish a durable moat.

Scenario B: US breakthrough accelerates growth

A US enterprise customer — a large 3PL, a major fashion retailer, or a consumer electronics distributor — deploys Nomagic at scale and provides a public reference that unlocks further US sales. This scenario requires Nomagic to navigate the competitive intensity of the US market, establish a local service infrastructure, and demonstrate that its European performance translates to US operational conditions. The probability of this scenario within 24 months is moderate; the US logistics market is large enough that a single significant win would be commercially transformative, but the barriers to that first win are substantial.

Scenario C: Acquisition

Nomagic is acquired by a larger industrial automation or logistics technology company seeking to add AI-driven manipulation capability to an existing portfolio. Potential acquirers could include a major conveyor or intralogistics systems integrator (Dematic, Vanderlande, Knapp), a robotics hardware manufacturer seeking a software and AI layer, or a logistics technology platform seeking to move into hardware. The precedent of ABB acquiring Covariant [not in dossier, editorial inference] and Stow Group acquiring Righthand Robotics suggests that the acquisition of AI-driven picking specialists by larger industrial players is an established pattern. Nomagic's European customer base and Physical AI positioning would be attractive to an acquirer seeking European market access.

Scenario D: Technology plateau and competitive displacement

VLA model development fails to deliver the promised step-change in SKU coverage and adaptability. Competitors with larger training data sets and more capital — particularly those backed by major industrial conglomerates — achieve comparable or superior performance and undercut Nomagic on price or service terms. Customer renewals slow, and the company is unable to raise a Series C on acceptable terms. This scenario is a real risk for any company in a capital-intensive technology market where the performance frontier is moving quickly. The absence of independent performance validation makes it difficult to assess how close Nomagic is to this scenario.

Scenario E: Regulatory friction delays US entry

US regulatory requirements — whether related to AI transparency, worker safety, or domestic content preferences in federal-adjacent procurement — create unexpected friction for Nomagic's US expansion. Combined with the competitive intensity of the US market, this delays meaningful US revenue by 36 months or more, putting pressure on the company's cash position and forcing a dilutive fundraise. This scenario is lower probability given Poland's NATO membership and the general openness of the US market to European technology companies, but it is not negligible given the current US regulatory posture on AI systems.

13What to Watch: A Live Monitoring Checklist

The following indicators are the most informative signals for tracking Nomagic's progress against the claims and scenarios outlined in this report. They are organised by category and time horizon.

Commercial validation (near-term, 0–12 months)

• First named US customer announcement. A public reference from a US enterprise operator would confirm that the US expansion is progressing beyond sales conversations. Watch for press releases, trade show announcements, or customer testimonials at ProMat or MODEX.
• Customer renewal or expansion announcements from existing European accounts. A named customer expanding from one solution to multiple (e.g., adding Pack or Sort to an existing Pick deployment) would indicate that operational performance is meeting expectations.
• Independent case study publication with quantitative performance data. Any customer-authored or independently audited case study that includes throughput rates, accuracy figures, or uptime data would substantially change the confidence level on Nomagic's performance claims.
• IFOY Award 2026 outcome for the Shoebox Picker. Whether the Shoebox Picker wins or loses the IFOY Innovation of the Year award will provide a partial independent signal on the product's technical credibility relative to competitors.

Technology development (medium-term, 6–24 months)

• VLA model publication or technical disclosure. If Nomagic publishes a technical paper, conference presentation, or detailed product documentation describing the VLA model architecture, training methodology, and benchmark performance, this would allow independent assessment of the AI claims. The current absence of any research output in the dossier is notable.
• SKU coverage methodology disclosure. Any public disclosure of how "95-plus percent SKU coverage" is measured — including the reference SKU universe, the definition of a "covered" SKU, and the conditions under which the system declines to attempt a pick — would allow buyers to assess the claim meaningfully.
• Third-party benchmark participation. Participation in an independent robotic picking benchmark (such as the Amazon Picking Challenge successor events or industry-organised benchmarks) would provide externally validated performance data.

Financial and organisational signals (ongoing)

• Series C fundraise announcement. The timing, size, and investor composition of a Series C would signal investor confidence in the US expansion and VLA development roadmap. A round led by US-based investors would indicate that the US market entry is gaining traction.
• US office or service infrastructure establishment. Job postings, office announcements, or partnership agreements with US-based systems integrators would indicate that the US expansion is moving from intention to execution.
• Key personnel changes. Departures or additions at the C-suite or VP level — particularly in sales, engineering, or AI research — are often leading indicators of strategic pivots or execution challenges.
• Revenue or customer count disclosure. Nomagic has not publicly disclosed revenue figures. Any disclosure — even directional — would allow more grounded assessment of commercial scale.

Competitive and market signals (ongoing)

• Competitor performance claims with independent validation. If a competitor publishes independently audited performance data that significantly exceeds Nomagic's claimed figures, this would be a material competitive signal.
• Large logistics operator in-house automation announcements. If ASOS, Zalando, or another named Nomagic customer announces a significant in-house or competing-vendor automation programme, this could signal dissatisfaction with Nomagic's performance or a strategic shift away from third-party robotics.
• EU AI Act compliance disclosures. As the EU AI Act's high-risk provisions take effect, watch for Nomagic's public statements on compliance posture, conformity assessment, and human oversight mechanisms.

14Sources and Methodology

Sources

¹ Nomagic | Physical AI for End-to-End Warehouse Automation — https://nomagic.ai/

² Pick | AI-Powered Robotic Picking | Nomagic — https://nomagic.ai/solution/picking/

³ Pack | Automated Robotic Packing | Nomagic — https://nomagic.ai/solution/packing/

⁴ Sort | High-Speed Robotic Sorting | Nomagic — https://nomagic.ai/solution/sorting/

⁵ No Magic MagicDraw UML | Dassault Systèmes — https://www.3ds.com/products/catia/no-magic/magicdraw (not cited; unrelated entity)

⁶ The Defense Industry Buying Guide for Cameo MBSE | GoEngineer — https://www.goengineer.com/defense-industry-guide-to-buying-cameo-mbse (not cited; unrelated entity)

⁷ Nomagic | Physical AI for End-to-End Warehouse Automation — https://nomagic.ai (duplicate of [1])

⁸ Nomagic secures an additional $10M to advance technology — https://nomagic.ai/news/nomagic-secures-an-additional-10m-to-accelerate-commercial-growth-and-advance-its-technology-roadmap

⁹ No Magic - Wikipedia — https://en.wikipedia.org/wiki/No_Magic (not cited; unrelated entity — refers to the UML modelling tool)

¹⁰ Nomagic raises $44M to scale European picking robot deployments — The Robot Report — https://www.therobotreport.com/nomagic-raises-44m-to-scale-european-picking-robot-deployments

¹¹ Nomagic Closes €8.51M Series B Extension to Scale AI-Driven Warehouse Robotics — The AI Insider — https://theaiinsider.tech/2026/02/05/nomagic-closes-e8-51m-series-b-extension-to-scale-ai-driven-warehouse-robotics

¹² Polish robotics startup Nomagic secures €8.3 million to scale Physical AI operations in the US — EU Startups — https://www.eu-startups.com/2026/01/polish-robotics-startup-nomagic-secures-e8-3-million-to-scale-physical-ai-operations-in-the-us

¹³ Nomagic nabs $22M for e-commerce warehouse picking robots — TechCrunch — https://techcrunch.com/2022/05/24/nomagic-picks-up-22m-for-its-e-commerce-warehouse-picking-robots

¹⁴ Reddit — r/dndnext (not cited; unrelated)

¹⁵ Reddit — r/programming (not cited; unrelated)

¹⁶ Reddit — r/Pathfinder2e (not cited; unrelated)

¹⁷ Reddit — r/mentalhealth (not cited; unrelated)

¹⁸ Reddit — r/RandomThoughts (not cited; unrelated)

¹⁹ Reddit — r/electriccars (not cited; unrelated)

Methodology

This report was produced by Max Robotics editorial analysts using a structured evidence-grading framework applied to a research dossier gathered on 22 June 2026. The dossier contained 20 source URLs across official, commerce, news, and community categories, of which 8 were substantively relevant to Nomagic the warehouse robotics company (sources ¹–⁴, ⁸, ¹⁰–¹³). Sources ⁵, ⁶, ⁹, and ¹⁴–¹⁹ were identified as referring to unrelated entities or topics sharing the "No Magic" name and were excluded from analysis.

Evidence grading

Four evidence grades are used throughout this report:

• Verified fact: confirmed by regulatory filing, official product documentation with independent corroboration, named-customer confirmation in an independent source, peer-reviewed research, or consistent reporting across multiple independent sources.
• Company claim: stated by Nomagic in official materials, press releases, or marketing content, without independent corroboration.
• Editorial inference: a reasoned conclusion drawn from the pattern of available evidence, clearly labelled as such.
• Unknown: information not publicly disclosed or not present in the evidence base.

Limitations

The research dossier contains zero research papers, zero video sources, and zero community sources relevant to Nomagic's technology. This means the report cannot assess the technical depth of Nomagic's AI claims against published research, cannot evaluate video evidence of operational performance, and cannot draw on user or operator community feedback. All specific performance figures (accuracy, coverage, uptime) originate from vendor sources only. The overall confidence score of 0.72 assigned by the dossier reconciliation process reflects these limitations.

The competitive landscape section draws on editorial knowledge of the warehouse robotics market that extends beyond the supplied dossier. Specific claims about named competitors are based on publicly available information but are not sourced to the dossier and should be independently verified before use in procurement or investment decisions.

This report reflects the state of publicly available evidence as of 22 June 2026. Nomagic's commercial and technical position may have changed materially since that date.