Rino.ai
About
About Rino.ai
Rino.ai, legally White Rhino Zhida, was founded in Beijing in 2019 by a team with autonomous-driving, commercial-vehicle and logistics experience. The company has concentrated on goods movement rather than passenger service and developed vehicle, autonomy, manufacturing and operations capabilities as an integrated stack.
Its R1, R3, R5, RX and R24 platforms cover neighbourhood delivery, campus and retail service, intra-city freight and higher-volume logistics. The autonomy system combines multi-sensor perception, vehicle-grade compute, prediction and planning, simulation, OTA updates and fleet dispatch. Rino reports deployments across more than 100 cities and partnerships with logistics and retail operators, while exact active fleet counts and customer economics vary by disclosure. The company belongs in autonomous logistics, not generic automotive or service robotics.
Articles
Operations
Output & shipments
- Deliveries
2026-10-08 - Rino reports cumulative delivery of more than 5,000 autonomous logistics vehicles across its portfolio. - Company-wide autonomous logistics vehicles delivered; not the active fleet or one model
Source
Deployments
- Operating use
2026-10-08 - Rino reports regular autonomous-logistics operations in more than 200 Chinese cities. - Chinese cities with regular operations; not vehicle or customer count
Source
Supply
Supply chain relationships
- RoboSense
Inbound - Perception - Rino.ai autonomous delivery vehicles use RoboSense robotics lidar
Source
Funding
- 2026-10-08 - Series C2 - Rino.ai closes C2 financing, bringing Series C total to $100 million
Hidden Hill Capital led the latest round for the autonomous-delivery vehicle company. - source: m.gasgoo.com
- 2026-05-06 - Series C1 - Rino.ai completes C1 financing for autonomous-delivery expansion
The round brought together a global logistics investor, Shenzhen Nanshan Zhanxintou and existing backer Yuanhe HouWang. - source: finance.eastmoney.com
Investors
- Hidden Hill Capital - Lead - VC - 1 funding event - first seen 2026-10-08
- Woori Financial Group - Participant - Unknown - 1 funding event - first seen 2026-10-08
- 元禾厚望 - Participant - Unknown - 1 funding event - first seen 2026-05-06
- 博正资本 - Participant - Unknown - 1 funding event - first seen 2026-10-08
Team
- Huang Gang - President - Leads product industrialisation and industry-policy engagement for Rino.ai's autonomous delivery business.
- Wang Hanji - Vice President - Represents Rino.ai's technology-plus-operations approach to commercial autonomous freight.
Timeline
Events
- 2026-10-08 - Funding - Rino.ai closes C2 financing, bringing Series C total to $100 million
Other backers include Xiangtan state-backed capital, 深重投, Hunan Caixin, Woori Financial Group and Bozheng Capital. Capital will support end-to-end Level 4 autonomy, Physical AI development and expansion of the urban operating network. Rino reports more than 5,000 autonomous logistics vehicles delivered and regular operations in over 200 Chinese cities.
Source - 2026-05-06 - Funding - Rino.ai completes C1 financing for autonomous-delivery expansion
Capital supports core technology, automotive-grade vehicle production and broader commercial applications. The company plans to expand same-city freight, instant delivery and overseas markets.
Source - 2026-01-28 - Deployment - Rino.ai begins R24 autonomous delivery vehicle pilot operations
The R24 provides more than 24 cubic metres of cargo volume, versus the 5-to-12-cubic-metre range Rino.ai cited for most autonomous delivery vehicles. Test operations began in a Shenzhen logistics park, moving the vehicle into a real operating environment. Rino.ai targets warehouse-to-store transfers and other high-volume short-haul routes, complementing its smaller RX, R3 and R5 cargo vehicles.
Source - 2026-01-16 - Product Launch - Rino.ai and Xinyuan begin RX robovan mass production
Rino.ai and Xinyuan Auto developed the RX through an automotive V-process, with vehicle-grade components and validation intended for large-scale commercial operations. The RX uses a platform-based modular design so different cargo modules can be fitted for multiple logistics scenarios.
Source