RECONOVA (07656.HK) released its first interim results since listing on August 26. During the reporting period, the company achieved operating revenue of approximately RMB 102 million, a year-on-year increase of 58.7%. Revenue growth was primarily driven by the synergistic contribution of its three business segments, with the core foundation remaining solid. Research and development expenses accounted for approximately 48.8% of operating revenue, with strategic investments building a robust technological moat for the company.

From a profitability perspective, the company recorded a net loss of approximately RMB 64 million during the reporting period, narrowing by 6.7% year-on-year. This improvement was achieved despite continued increases in R&D investment (up 51.2% year-on-year)—the company is in the investment phase of its embodied intelligence strategy, with R&D spending accounting for nearly half of revenue. The net loss primarily stems from incremental R&D investments related to embodied intelligence and one-off listing expenses for the current period. In terms of operational efficiency, while revenue scale expanded, selling and distribution expenses declined by approximately 5.3% year-on-year, reflecting continuous efficiency optimization. As the net loss narrowed, accounts receivable also fell to approximately RMB 510 million, down 13.0% from the end of the prior year, indicating significantly improved collection efficiency.

Looking at future visibility, the company's contract liabilities stood at approximately RMB 12 million at the end of the reporting period, up 420% from the beginning of the year. Contract liabilities represent advance payments received from customers for which the company has not yet delivered. These paid-but-undelivered orders will be gradually recognized as revenue in subsequent accounting periods, directly enhancing future revenue visibility. For a technology company in its strategic investment phase, the trend in contract liabilities often provides a better indication of true business direction than current-period net profit.

All three business segments achieved year-on-year growth during the reporting period, with continuous structural optimization and the core business demonstrating sustainable cash-generation capability. The smart civil aviation segment generated revenue of approximately RMB 3 million, up 319.1% year-on-year, benefiting from enhanced customer stability. The company maintains the No.1 market share in visual intelligence products for civil aviation enterprises, covering over 60% of airports with annual passenger throughput exceeding 10 million nationwide, with benchmark projects continuously replicated and iterated. It is worth noting that revenue in the civil aviation industry exhibits significant seasonality—affected by airport annual budget execution pace and project acceptance cycles, the company's revenue recognition in this segment tends to concentrate in the second half, particularly in the fourth quarter. The smart commerce segment accelerated the acquisition and servicing of high-value customers, driving year-on-year growth in average customer value and transaction volume, achieving revenue of approximately RMB 34 million, up 95.5% year-on-year. The Xinghan commercial solutions for commercial spaces accelerated deployment, achieving rapid growth with increasingly diversified revenue sources. The smart safe driving segment generated revenue of approximately RMB 66 million, up 41.2% year-on-year. New customer order scale grew significantly, driving sales expansion.

RECONOVA has深耕 visual intelligence for fourteen years and has built an integrated enterprise-grade embodied intelligence technology system spanning "perception→cognition→physical execution," steadily advancing the scaled commercialization of commercial robots for "productivity scenarios." In terms of specific progress, the airport luggage transfer robot "Xiao Yi," launched in September 2025, has completed pilot deployment at a domestic airport with annual passenger throughput exceeding 10 million, supporting 7x24-hour continuous operations. In April 2026, the company iteratively launched the new VTFLA embodied intelligence technology architecture, which integrates visual, tactile, and force multi-modal input perception on top of traditional VLA frameworks. The core design philosophy is that over 80% of intelligence is realized at the edge, employing a fast-slow collaborative dual-system design to achieve decoupled coordination between task planning and real-time control. The company plans to further advance the development of edge-side lightweight embodied intelligence technology, continuously enhancing multi-modal perception and execution capabilities. At the recently concluded 2026 World Robot Conference, the company presented a complete embodied intelligence solution for airport productivity scenarios, reconstructing traditional manual transfer models through human-machine systems and multi-robot collaborative operations, creating a safe, reliable, and flexibly adaptable full-chain intelligent luggage transfer system. Additionally, the company has initiated R&D on wheeled dual-arm robots, expected to launch in 2027 and achieve commercialization by 2028. Application scenarios will extend from fixed luggage handling to mobile loading/unloading beneath aircraft, gradually expanding into warehousing and industrial automation. During the exhibition, the company also signed a strategic cooperation agreement with Shangshi Technology to jointly build the Hong Kong Embodied Intelligence Super Accelerator, promoting industrial implementation and international expansion.

In terms of internationalization, the company adopts a "near-first, far-second" strategy, prioritizing expansion into Southeast Asian and Middle Eastern markets. Currently, the company has launched POC pilot projects at Hamad International Airport in Doha, Qatar, and Tashkent Airport in Uzbekistan. The implementation path follows the "head customers first, long-tail later" principle, with leading airports in the Middle East and Southeast Asia as the first-phase focus. After establishing benchmark cases, the company will gradually radiate to long-tail customers before progressively entering European and American markets. The company is replicating the visual AI and embodied intelligence experience accumulated in domestic airports to global markets that are investing heavily in smart airport construction. In terms of cooperation models, the company emphasizes local ecosystem collaboration and technology standard output, combining product localization with an asset-light, deep-cooperation approach. Approximately 10.4% of the IPO proceeds have been specifically allocated to overseas sales channel development, providing financial support for the globalization strategy.

The company adheres to the strategic principle of "extreme convergence, extreme focus," concentrating on "productivity tools" in material handling, palletizing/depalletizing, and specialized scenarios, rather than pursuing generalized concepts such as consumer-grade robots or general-purpose humanoid robots. In the short term, the three core business pillars—smart civil aviation, smart commerce, and smart safe driving—will continue to generate stable cash flow. In the medium to long term, leveraging its cross-scenario reusable general visual intelligence foundation, the company will continue to strengthen its full-stack embodied intelligence core technology system, iterate multi-category commercial robots, and complete the strategic upgrade from visual intelligence solutions to integrated enterprise-grade embodied intelligence encompassing "perception-cognition-physical execution." Currently, embodied intelligence has been included in the national "15th Five-Year Plan" as a key future industry for cultivation. According to Frost & Sullivan forecasts, China's enterprise-grade visual intelligence market will exceed RMB 162.7 billion by 2030. As B-end customer demand for cost reduction and efficiency improvement gradually rises and the technology maturity curve crosses the inflection point, commercial robots are expected to enter a window of concentrated demand release in productivity scenarios such as airport luggage handling, warehousing and logistics, and industrial automation over the next 3 to 5 years. Leveraging its fourteen years of accumulated visual intelligence foundation and multi-scenario engineering delivery experience, RECONOVA is accelerating the entry of embodied intelligence into real industrial scenarios, completing the transition from visual intelligence to enterprise-grade embodied intelligence, with long-term growth potential gradually unfolding.