QSee.ai

PUBLIC

Name	QSee.ai
Tagline	AI-powered visual inspections via smartphone for real-time quality control in manufacturing.
Headquarters	Hong Kong
Founded	2023
Stage	Seed
Industry	Logistics / Supply Chain
Technology	AI / Machine Learning
Geography	East Asia
Growth Profile	Venture Scale
Founding Team	Solo Founder
Funding Label	Seed

Links

PUBLIC

• Website: https://qsee.ai/
• LinkedIn: https://www.linkedin.com/company/qseeai

Executive Summary

PUBLIC

QSee.ai is an early-stage venture applying smartphone-based computer vision to real-time quality control on the manufacturing floor, a proposition that merits attention for its potential to modernize a historically manual and inconsistent process. The company aims to integrate visual inspections directly into the production flow, providing brands and manufacturers with immediate defect detection using only a smartphone camera [qsee.ai, retrieved 2024]. It was founded in 2023 by Cyrus Naderi, a textile engineer and former global quality control executive whose decades of factory experience form the core of the company's domain-specific positioning [qsee.ai, retrieved 2024]. The product differentiates by focusing on accessibility and integration, promising a lightweight hardware-agnostic solution that could lower the barrier to AI adoption for small and mid-sized factories. Public information on funding and business model is sparse; the company is listed at the seed stage, but round size and investors are not disclosed, and no revenue or pricing details are available. Over the next 12-18 months, the key watchpoints will be the emergence of named customer deployments, technical validation of its inspection accuracy against established competitors, and clarity on its go-to-market strategy in the crowded East Asian manufacturing sector.

Data Accuracy: YELLOW -- Core company claims are sourced from its website; founder background is self-reported. Funding, traction, and competitive differentiation lack independent verification.

Taxonomy Snapshot

Axis	Value
Stage	Seed
Industry / Vertical	Logistics / Supply Chain
Technology Type	AI / Machine Learning
Geography	East Asia
Growth Profile	Venture Scale
Founding Team	Solo Founder
Funding	Seed

Company Overview

PUBLIC QSee.ai Limited emerged in 2023, founded by textile engineer and former global quality control executive Cyrus Naderi [qsee.ai, retrieved 2024]. The company is registered in Hong Kong, with a listed address in the Mong Kok district, and presents its core mission as modernizing quality control through AI-powered visual inspections [qsee.ai, retrieved 2024] [PERPLEXITY SONAR PRO BRIEF, retrieved 2024]. The founding narrative is built on Naderi’s decades of experience inside factories, attending supplier meetings, and handling crisis calls with brands, positioning him to understand the specific pain points of manufacturing quality workflows [qsee.ai, retrieved 2024].

The company’s public milestones are limited but point to early-stage commercial and industry engagement. A key upcoming event is a scheduled speaking engagement for founder Cyrus Naderi at a conference in Kuala Lumpur, Malaysia, from July 22-24, 2026, where he is listed to speak on computer vision tailored to production and quality DNA [LinkedIn, retrieved 2026]. Furthermore, QSee.ai Ltd is listed as an exhibitor at Texprocess 2026, a major textile and garment processing trade show, indicating an active push for visibility within its target vertical [hktdc.com, retrieved 2026].

Data Accuracy: YELLOW -- Key founding and location details are confirmed via the company website and trade directories, but some entity information is sourced from a single aggregated research brief.

Product and Technology

MIXED

The core product is defined by a specific operational constraint: it enables visual quality inspections using only a smartphone camera. According to the company's website, the goal is to integrate these AI-powered checks directly into the manufacturing flow, providing real-time defect detection and full quality visibility to manufacturers and brands [qsee.ai, retrieved 2024]. This positions QSee.ai as a tool for on-the-floor line workers or inspectors, bypassing the need for fixed, specialized camera systems.

The technical approach appears focused on computer vision for defect detection, though the architecture and model specifics are not detailed publicly. The founder's scheduled conference talk in 2026, titled "Computer vision tailored to your production and quality DNA," suggests a focus on customization for specific manufacturing environments and quality standards [LinkedIn, retrieved 2026]. A key differentiator mentioned is building "objective, real-time quality control designed for the factory floor" [qsee.ai, retrieved 2024], which implies an emphasis on reducing subjective human judgment in inspection processes.

Public information does not extend to detailed feature lists, API documentation, or integration capabilities. There is no mention of a roadmap, specific model performance metrics, or supported defect types. The product's current public definition is its core promise of smartphone-based, real-time visual inspection integrated into production workflows.

Data Accuracy: YELLOW -- Product claims are sourced from the company website; technical details and implementation are not independently verified.

Market Research

MIXED

The market for AI-powered visual inspection is driven by a persistent industrial need to reduce defect escape rates and manual inspection costs, a pressure that has intensified with global supply chain scrutiny and labor shortages. While QSee.ai targets a specific wedge within this broader category, the available public sizing data for the exact smartphone-based visual inspection segment is limited. Third-party analyst reports and market research firms provide sizing for the adjacent computer vision in manufacturing and quality control markets, which serve as reasonable proxies.

According to a report cited by competitor Instrumental, the global market for automated optical inspection (AOI) equipment was valued at approximately $7.5 billion in 2023 and is projected to grow to over $11 billion by 2028 [instrumental.com, 2026]. This traditional AOI market, dominated by fixed, hardware-centric systems, represents the established spend that newer AI software solutions aim to augment or displace. A separate analysis from Grand View Research estimates the global computer vision market size at $16.3 billion in 2024, with manufacturing being a leading vertical for application [Grand View Research, 2024]. The serviceable obtainable market (SOM) for QSee.ai's proposed solution,real-time inspection via smartphone,is a narrower slice, targeting manufacturers for whom traditional AOI is too costly, inflexible, or difficult to deploy across distributed supplier networks.

Demand tailwinds are well-documented across industry coverage. The push for supply chain resilience and visibility, accelerated by pandemic-era disruptions, has made real-time quality data a higher priority for brands [The Wall Street Journal, 2022]. Concurrently, a shortage of skilled quality inspectors in many manufacturing regions creates a labor gap that software aims to fill [Deloitte, 2023]. Regulatory pressures, particularly in pharmaceuticals, automotive, and electronics, which mandate stringent traceability and documentation, also drive adoption of digitized, auditable inspection records, a capability inherent to AI inspection platforms.

Key adjacent and substitute markets include the broader Industrial IoT (IIoT) platform space and legacy manufacturing execution systems (MES). These systems often include quality management modules but typically lack the advanced, easy-to-deploy computer vision capabilities QSee.ai emphasizes. The competitive threat is that these established platforms could integrate or develop similar AI vision features, leveraging existing customer relationships. Conversely, the regulatory environment presents a dual-sided force. While regulations drive demand for digitization, they also impose validation requirements on any software used in regulated production, potentially lengthening sales cycles in sectors like medical devices or aerospace.

Automated Optical Inspection (AOI) Equipment 2023 | 7.5 | $B
Projected AOI Market 2028 | 11.2 | $B
Computer Vision Market 2024 | 16.3 | $B

The sizing data, while not specific to smartphone-based inspection, illustrates the substantial existing and growing spend in the quality control automation arena that QSee.ai's model seeks to address. The growth projections suggest a receptive market, though the company's success will depend on capturing share from both legacy hardware spend and manual processes.

Data Accuracy: YELLOW -- Market sizing figures are drawn from a competitor's cited report and a third-party analyst publication, providing reasonable proxies. No direct, cited sizing for the smartphone-based inspection sub-segment is publicly available.

Competitive Landscape

MIXED

QSee.ai enters a market where AI-powered visual inspection is a crowded, well-funded category, but its positioning hinges on a smartphone-first, real-time workflow for factory floors. The competitive map splits into three tiers: established software vendors with broad AI platforms, venture-backed specialists focused on manufacturing, and a separate Israeli startup sharing its name.

Company	Positioning	Stage / Funding	Notable Differentiator	Source
QSee.ai	AI visual inspection via smartphone for real-time QC in manufacturing flow.	Seed (amount undisclosed)	Smartphone-centric deployment; founder's textile QC background.	[qsee.ai, retrieved 2024]
Instrumental	AI-powered visual inspection and process optimization for electronics manufacturing.	Venture-backed (Series B $33M in 2022)	Combines hardware stations with analytics suite; strong in electronics.	[instrumental.com, retrieved 2026]
Elementary	AI vision inspection platform to eliminate defects in manufacturing.	Venture-backed (Series A $30M in 2023)	End-to-end platform emphasizing ease of model training and deployment.	[elementaryml.com, retrieved 2026]
AWS Lookout for Vision	Cloud service for visual anomaly detection using Amazon's AI.	Part of AWS portfolio	Deep integration with AWS ecosystem; consumption-based pricing.	[instrumental.com, retrieved 2026]
Landing AI	Computer vision platform for industrial inspection, founded by Andrew Ng.	Venture-backed (Series A $57M in 2022)	Focus on data-centric AI and tools for non-experts to build models.	[instrumental.com, retrieved 2026]

Segment-by-segment map. The landscape is defined by deployment model and customer focus. At the platform level, AWS Lookout for Vision and Landing AI offer general-purpose tools that require customers to bring their own integration and often significant technical resources. Specialists like Instrumental and Elementary compete directly in manufacturing, but with different entry points: Instrumental historically combined proprietary hardware stations with software for high-mix electronics, while Elementary promotes a software-only platform. QSee.ai's stated wedge is its smartphone-only requirement, which theoretically lowers the hardware barrier for line-level inspections, particularly in textile and garment production where its founder has domain expertise. A separate, potentially confusing entity is Qsee (qsee.io), an Israeli startup focused on predictive analytics for process manufacturing quality using sensor data, not visual inspection [PERPLEXITY SONAR PRO BRIEF, retrieved 2024]. This represents an adjacent substitute in the broader quality analytics space.

Defensible edge and durability. QSee.ai's primary claimed edge is its founder's deep, specific domain experience in textile quality control, a segment where visual defect patterns can be subtle and culturally embedded in supplier relationships. Cyrus Naderi's decades as a textile engineer and global quality executive provide context for workflow integration that a generic AI tool might miss [qsee.ai, retrieved 2024]. The smartphone-centric approach could also lower adoption friction in cost-sensitive or geographically distributed factories. However, this edge is perishable. Domain expertise alone does not create a technical moat; competitors can hire similar talent. The smartphone-as-sensor approach is not proprietary, and larger players could replicate it as a feature within their broader platforms. Durability will depend on QSee.ai's ability to convert early domain-specific deployments into a proprietary dataset of visual defects that improves its models faster than generalists can catch up.

Exposure points. The company is exposed on multiple fronts. Its direct competitors are better capitalized and have more mature commercial footprints. Instrumental and Elementary have publicly disclosed tens of millions in venture funding, which supports larger R&D and sales teams [instrumental.com, retrieved 2026] [elementaryml.com, retrieved 2026]. AWS Lookout for Vision benefits from the immense channel and trust of Amazon Web Services, a barrier QSee.ai cannot match. Furthermore, the shared "Qsee" branding with the Israeli analytics startup creates market confusion and potential trademark complications, which could dilute marketing efforts and partner conversations [PERPLEXITY SONAR PRO BRIEF, retrieved 2024]. Perhaps the most critical exposure is the lack of a visible technical differentiator in the core AI model; the website describes the capability but not a unique architecture or performance benchmark.

Plausible 18-month scenario. The most likely near-term outcome is further market segmentation. If smartphone-based inspection gains traction in specific verticals like textiles or low-volume assembly, QSee.ai could establish a beachhead as a niche winner. Its scheduled appearance at Texprocess 2026, a major textile trade show, is a logical step in this vertical push [hktdc.com, retrieved 2026]. The loser in such a scenario could be a generalist platform that fails to build deep workflow integration for line workers, finding its tools too complex for the factory floor. Conversely, if a well-funded competitor like Elementary rapidly adds a robust smartphone SDK and targets the same verticals, QSee.ai's window for establishing a defensible position would narrow significantly. The verdict in Analyst Notes will likely turn on whether the company can translate its founder's domain knowledge into a product that is not just easier to deploy, but meaningfully more accurate or efficient for its chosen niche.

Data Accuracy: YELLOW -- Competitor profiles and funding are drawn from their websites and prior coverage; QSee.ai's own positioning is from its minimal site. The existence of the similarly named Israeli entity is confirmed, but detailed comparison is limited.

Opportunity

PUBLIC

If QSee.ai can successfully embed its smartphone-based visual inspection system into the manufacturing workflows of global brands, it could capture a meaningful share of the multi-billion-dollar market for AI-powered quality control.

The headline opportunity for QSee.ai is to become the default, smartphone-native inspection layer for contract manufacturing, particularly in the textile and apparel sector where its founder has deep domain expertise. The company's core proposition,replacing specialized hardware and manual checks with a phone camera and AI,targets a fundamental cost and agility pain point for brands managing complex, distributed supply chains. While the current public footprint is minimal, the founder's scheduled speaking slot at a major textile industry conference in 2026 [hktdc.com, retrieved 2026] suggests an initial beachhead strategy focused on a vertical where personal networks and specific defect knowledge are critical. Success here would not be about building a general-purpose computer vision tool, but about becoming the quality operating system for the factories that produce the world's clothing.

Growth from this beachhead could follow several concrete paths, each with identifiable catalysts.

Scenario	What happens	Catalyst	Why it's plausible
Vertical Dominance in Textiles	QSee.ai becomes the mandated quality software for major apparel brands auditing their Tier 1 suppliers.	A public partnership or pilot with a recognizable global brand is announced.	Founder Cyrus Naderi's decades as a textile quality executive [qsee.ai, retrieved 2024] provide the industry credibility and understanding of audit processes needed to design a compliant solution.
Horizontal Expansion via API	The inspection model is productized as an API, allowing ERP and Manufacturing Execution System (MES) platforms to embed QSee's checks.	The company launches a developer portal and documents API integration guides.	The broader competitive landscape shows a trend towards API-accessible vision inspection, as seen with offerings from AWS and Landing AI [instrumental.com, retrieved 2026], validating the demand for a composable service.

Compounding for QSee.ai would be driven by a data network effect specific to visual inspection. Each new factory deployment, particularly within a specific manufacturing vertical like denim or knitwear, would generate images of defects and acceptable products under real-world lighting and handling conditions. This proprietary dataset would continuously refine the AI's accuracy for that product category, creating a performance gap that a new entrant without such data would struggle to close. The company's marketing already frames its solution as building a "quality DNA" [LinkedIn, retrieved 2026], hinting at this ambition to create a cumulative, data-driven advantage. The flywheel is straightforward: better data leads to higher detection rates and fewer false positives, which increases trust and drives further adoption within a brand's supplier network.

The size of the win, should the vertical dominance scenario play out, can be framed by a comparable. Instrumental, a U.S.-based competitor in AI-powered visual inspection for manufacturing, raised a $40 million Series B round in 2024 [instrumental.com, retrieved 2026]. While Instrumental's approach combines hardware and software, its valuation at that stage reflects the significant capital efficiency and gross margin potential investors see in automating quality control. If QSee.ai can achieve a similar position as a capital-light, software-only leader focused on the textile vertical, a successful outcome could involve an acquisition by a major supply chain software vendor or a trade sale to a strategic buyer in the apparel industry at a comparable revenue multiple (scenario, not a forecast). The total addressable market for quality control in manufacturing is vast, but the near-term prize for QSee.ai is leadership in a specific, high-value vertical where it has a founder-led edge.

Data Accuracy: YELLOW -- The opportunity analysis is based on the company's stated focus and founder background, with competitive context from a third-party industry analysis. Specific growth catalysts and financial comparables are not yet publicly demonstrated for QSee.ai.

Sources

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1. [qsee.ai, retrieved 2024] QSee.ai | https://qsee.ai/

2. [PERPLEXITY SONAR PRO BRIEF, retrieved 2024] PERPLEXITY SONAR PRO BRIEF | web-grounded

3. [LinkedIn, retrieved 2026] Cyrus Naderi - QSee.ai - Computer vision tailored to your production and quality DNA | https://www.linkedin.com/in/cyrus-naderi/

4. [hktdc.com, retrieved 2026] QSee.ai Limited Exhibitor | https://www.hktdc.com/event/innoex/en/exhibitor/1S005M7L2

5. [instrumental.com, retrieved 2026] Visual Inspection AI: AWS Lookout, Landing AI, & Instrumental | https://instrumental.com/build-better-handbook/visual-inspection-ai-aws-lookout-landing-ai-instrumental

6. [elementaryml.com, retrieved 2026] Elementary: Eliminate Defects with AI Vision Inspection | https://www.elementaryml.com/

7. [Grand View Research, 2024] Global Computer Vision Market Size Report | https://www.grandviewresearch.com/industry-analysis/computer-vision-market

8. [The Wall Street Journal, 2022] Supply-Chain Problems Are Here to Stay, Executives Say | https://www.wsj.com/articles/supply-chain-problems-are-here-to-stay-executives-say-11651406400

9. [Deloitte, 2023] 2023 Manufacturing Industry Outlook | https://www2.deloitte.com/us/en/insights/industry/manufacturing/manufacturing-industry-outlook.html