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Ainos, Inc. reports developments tied to its transition toward AI-powered scent intelligence and industrial sensing. The company centers its commercial focus on AI Nose, an electronic olfaction platform that uses sensor arrays and proprietary artificial intelligence models, including its Smell Language Model, to convert volatile organic compound and scent signals into machine-readable Smell ID data.
Recurring news themes include AI Nose commercialization across semiconductor manufacturing, robotics, smart infrastructure, and healthcare-related environments; distribution and deployment relationships; industry classification updates; and financial results connected to the platform. Ainos also maintains a therapeutic-development program around VELDONA, a low-dose oral interferon program targeting rare, autoimmune, and infectious diseases.
Ainos (NASDAQ:AIMD) announced the global launch of Atmosphere Engine™, a modular platform that generates high-purity nitrogen and oxygen from ambient air on-site, extending the company’s AI-powered environmental sensing and Chemical Intelligence into active environmental management for AI infrastructure and semiconductor manufacturing.
The system is designed to create nitrogen-rich inert environments to help reduce oxidation and environmental degradation around high-value computing and fab equipment, while its building-block architecture allows capacity to scale from individual tools or zones to larger facilities. According to Ainos, engineering calculations indicate Atmosphere Engine is designed to use roughly one-third of the electricity of conventional molecular sieve-based systems under comparable conditions.
Ainos (NASDAQ:AIMD) announced the second generation of its AI Nose platform, combining upgraded software, firmware, Edge AI and industrial-grade hardware engineered for continuous 24/7 deployment in commercial and industrial environments. Each device acts as an intelligent edge node, locally processing chemical data and feeding Ainos' cloud-connected Smell AI network.
Ainos reported its Smell AI network has accumulated more than 878 million real-world smell data records, forming a growing foundation for what the company defines as Chemical Intelligence—digitizing complex chemical signatures so machines can analyze and learn from environmental changes across sectors such as semiconductor manufacturing, data centers, smart buildings, industrial safety, healthcare environments and robotics.
Ainos (NASDAQ:AIMD) reported that its AI Nose platform has surpassed 878 million real-world smell and chemical data points as of August 13, 2026, up from about 613 million on July 21. The dataset expanded by roughly 265 million points in 23 days, an increase of about 43%, primarily from semiconductor manufacturing and operating environments.
The company is advancing a planned deployment of 1,400 AI Nose systems, with additional systems being installed as Physical Edge Nodes. These nodes feed proprietary data into the ScentAI continuous learning architecture, supporting Ainos’ strategy to build Chemical Intelligence as a foundational layer for Physical AI.
Ainos (NASDAQ:AIMD) announced it will showcase its Environmental Intelligence solutions for Physical AI at SEMICON Taiwan 2026, held September 2-4 at Taipei Nangang Exhibition Center, Hall 1, Booth I2700, together with distribution partner Topco Scientific.
The company will demonstrate its AI Nose environmental sensing platform and Atmosphere Engine environmental control solution. These technologies aim to let intelligent machines sense chemical signals in the air, convert them into digital Smell ID data, and connect environmental understanding with control to help systems sense, understand, and optimize their surroundings in semiconductor manufacturing, robotics, industrial and healthcare environments.
Ainos (NASDAQ:AIMD) announced that research firm VASRO highlighted the company’s progress in scaling its AI Nose platform through semiconductor deployments, real-world data accumulation, and commercialization into the second half of 2026. According to VASRO, AI Nose has gathered about 613 million industrial Smell ID data records since December 2025, mainly from semiconductor manufacturing.
VASRO cites a $2.1 million AI Nose subscription backlog from a backend semiconductor customer, including roughly $350,000 prepaid, plus ongoing validation in front-end fabs. The report views the expanding deployment footprint, dataset growth, and commercial commitments as constructive indicators for broader Smell AI adoption and potential revenue growth.
Ainos (NASDAQ:AIMD) announced that Zacks Small-Cap Research has published a report covering the company’s AI Nose platform and its broader Smell AI strategy. The report reviews how AI Nose, Smell ID, and Ainos’ Smell Language Model (SLM) convert airborne chemical signals into structured digital scent data for artificial intelligence applications.
According to Ainos, the report highlights efforts to build a Smell Intelligence Network, using deployed AI Nose systems to generate real-world scent data, initially focused on semiconductor manufacturing. It also discusses potential uses in robotics and healthcare, including digital breath intelligence, and describes AI Nose’s SmellTech-as-a-Service model for subscription-based scent intelligence.
Ainos (NASDAQ:AIMD) reported second-quarter 2026 revenue of $152, down from $4,663 a year earlier, with a net loss of $4.59 million versus $4.08 million. For the first half of 2026, revenue was $313 and net loss was $7.05 million.
Operating expenses for Q2 2026 were $4.40 million, slightly above $3.75 million in Q2 2025. Cash and cash equivalents rose to $1.42 million from $0.42 million at December 31, 2025, while total liabilities increased to $16.28 million and stockholders’ equity fell to $3.28 million.
Ainos highlighted continued commercialization of its AI Nose platform, including progress on a previously announced initial $2.1 million semiconductor manufacturing arrangement, expanded deployments and pilots across semiconductor, industrial, healthcare infrastructure, and robotics, and growth of its industrial smell dataset to about 613 million records since December 2025.
Ainos (NASDAQ:AIMD) reported that its AI Nose deployments have generated approximately 613 million industrial scent data points since December 2025, mainly from semiconductor manufacturing environments. These data streams feed proprietary Smell IDs and strengthen the company’s Smell Language Model (SLM), supporting its long-term vision of a global smell intelligence network.
AI Nose digitizes scent into machine-readable Smell ID using high-precision sensor arrays and proprietary AI algorithms, targeting ppb-level sensitivity subject to deployment conditions. Offered under a SmellTech-as-a-Service subscription model, the platform is designed for continuous monitoring, predictive analysis, and real-time alerts across industrial, robotics, infrastructure, healthcare, and other physical AI applications.
Ainos (NASDAQ:AIMD) announced that VASRO Research published an update highlighting Ainos' planned expansion of its AI Nose platform into patient-level breath intelligence. The one-year research program with National Taiwan University, starting July 1, 2026, will evaluate AI-analyzed breath patterns in patients with shortness of breath.
The study focuses on distinguishing acute exacerbations of COPD from acute decompensated heart failure, conditions that may appear similar but need different clinical responses. According to Ainos, digitized breath scent data may expand real-world inputs for its Smell Language Model (SLM) across diverse use cases.
Ainos (NASDAQ:AIMD) launched a one-year research program with National Taiwan University to extend its Smell AI platform from environmental sensing into digital breath intelligence for emergency medicine. The study will analyze exhaled VOCs to create AI-based breath-print Smell IDs for dyspneic patients.
The program will investigate whether VOC patterns can support a Deep Dyspnea Differential system to help distinguish AECOPD, ADHF, and control groups. Activities include prospective enrollment, breath sampling, deep learning model training, and external validation against final clinical diagnoses. The technology and workflow remain research-stage.