ARCBOS 全电 · 全自治 · 工业级极寒除雪机器人

ARCBOS
ENGINEERED FOR EXTREME CONDITIONS

Snow removal is not an equipment problem.
It is an industrial autonomy problem under extreme environments.

We are not building a single-purpose machine.
We are building an industrial-grade autonomous operating capability that can run reliably in extreme outdoor conditions.

Project: SnowBot
Product Positioning: All-electric · Autonomous · Industrial-grade extreme-cold snow-removal robotic system
Target Markets: North America, Canada, Europe, and commercial / industrial facility operations in cold regions of China
Financing Stage: Seed Round / Engineering Prototype Validation
Target Raise: US$2 million, or approximately RMB 15 million equivalent
Equity: To be negotiated based on valuation and investment structure

Core of this round: not short-term revenue, but an engineering inflection point.

Seed Round objective:
Build an operational, verifiable, demonstrable industrial engineering prototype that can enter pilot-readiness.

Valuation step-up logic:
Concept validation
└─> Engineering validation
    └─> Scenario validation
        └─> Small-batch commercialization

This round does not buy financial statements.
It buys engineering certainty:
├─ Extreme-cold startup and thermal-management capability
├─ Heavy-duty chassis and real snow-removal execution capability
├─ System integration across perception, control, and actuation
├─ Remote monitoring, fault alerts, and degraded-operation capability
└─ Qualification for customer demonstrations and winter pilot entry

More importantly:
This round validates whether ARCBOS can establish the first engineering foundation for an industrial-grade autonomous system under extreme environments.

SnowBot is the first entry point.
Behind it, we are accumulating:
├─ Extreme-cold operating capability
├─ Multi-system integration capability
├─ Autonomous decision-making and safe-degradation capability
├─ Long-duration operation and remote operations capability
└─ Shared platform capability for future mowing, inspection, and security applications

ARCBOS is not a single-product snow-removal equipment company.
It is a robotics company entering through extreme-environment operations to build industrial-grade autonomous system capability.

SnowBot is ARCBOS’s first product and the first scenario for validating extreme-environment industrial autonomy.

Company Information:
├─ Legal Name: Shanghai ARCBOS Technology Co., Ltd.
├─ English Name: Shanghai ARCBOS Systems Ltd
├─ Registered Address: 7F, No. 348 Gongyuan Road, Qingpu District, Shanghai, China
├─ Registered Capital: RMB 1 million
├─ Date of Establishment: April 9, 2026
└─ Legal Representative: Andy Gong

The company has been established.
The current focus is SnowBot engineering prototype validation and building the first capability foundation.

Long-term direction:
├─ Establish the first engineering foundation through extreme-cold snow removal
├─ Expand into high-reliability outdoor scenarios such as mowing, inspection, and security
├─ Build an industrial-grade autonomous system platform for extreme environments
└─ Ensure company value is not limited to a single device or seasonal use case

At the current stage, the company is focused on SnowBot engineering prototype validation.
It will not pursue multiple product lines at once.
The priority is to build the first operational, verifiable, and repeatable system capability foundation.

Snow removal is not discretionary consumption.
It is a mission-critical facilities operations requirement.

Typical target customer scenarios:
├─ Data centers
├─ Medical campuses
├─ Logistics parks
├─ Industrial facilities
├─ Commercial complexes
├─ Educational institutions
└─ Public facility support areas

These markets share four characteristics:
├─ Budgeted
├─ Recurring every year
├─ Non-optional
└─ High consequence of failure

Current mainstream approach:
Human dispatch + fuel-powered equipment + experience-driven execution + non-auditable results

Common pain points:
├─ Heavy dependence on labor
├─ Difficult night-time dispatch
├─ Unstable efficiency under low-temperature conditions
├─ Volatile cost structure
├─ Non-quantifiable operating quality
└─ Lack of remote monitoring and process traceability

The market does not lack equipment.
It lacks an industrial-grade autonomous operating system that can run reliably over time in extreme environments.

Why this category is investable:
├─ Real demand
├─ Mature spending
├─ Clear replacement logic
├─ Defined scenarios
├─ High technical threshold
└─ Platform expansion potential once validated

The window is created by four converging trends:
├─ Rising labor cost
├─ Increasing customer demand for auditability
├─ Maturing electric drive, perception, and autonomy technologies
└─ Clear North American procurement and pilot cycles

Key customer concerns:
├─ LOS (Level of Service)
├─ Response time
├─ Coverage rate
├─ Auditability
└─ Risk control

The opportunity today is not simply that robots can now be built.
It is that extreme-environment work can begin to migrate from labor- and experience-driven operations into industrial-grade autonomous systems.

SnowBot is not a single mechanical device.
It is an industrial-grade autonomous system for extreme-cold snow-removal operations.

System objectives:
├─ Start reliably under extreme-cold conditions
├─ Operate under complex snow conditions
├─ Remain monitorable during long-duration operation
├─ Degrade safely during faults
└─ Be verifiable on customer sites

Six core SnowBot systems:
├─ Extreme-cold operating system
│  ├─ Low-temperature startup
│  ├─ Battery thermal insulation
│  ├─ Thermal-management loop
│  └─ Temperature control for critical electronics
│
├─ All-electric drive system
│  ├─ High-torque output
│  ├─ Traction and braking control
│  ├─ Low-temperature power retention
│  └─ Anti-freezing and heavy-duty adaptation
│
├─ Work-execution system
│  ├─ Snow pushing / clearing execution
│  ├─ Anti-clogging design
│  ├─ Parameter matching
│  └─ Modular work-interface architecture
│
├─ Perception and localization system
│  ├─ Multi-source perception
│  ├─ Non-vision-only architecture
│  ├─ Boundary recognition
│  └─ Low-visibility safety strategy
│
├─ Autonomous control system
│  ├─ Path execution
│  ├─ Priority management
│  ├─ Anomaly handling
│  └─ Fault-tolerant mechanisms
│
└─ Remote operations system
   ├─ Status telemetry
   ├─ Fault alerts
   ├─ OTA capability
   └─ Logs and traceability

This prototype is not meant to prove a concept.
It must prove the following capabilities:
├─ Complete the basic snow-removal workflow
├─ Start and operate for short continuous periods under low-temperature conditions
├─ Execute basic paths and perform safe stops
├─ Provide remote status monitoring and key parameter telemetry
└─ Enter customer demonstration or pre-winter pilot-readiness validation

To keep milestones clear, this round will not pursue:
├─ Large-scale mass production
├─ A complete commercial loop
├─ Fully generalized autonomy across all scenarios
├─ Long-term fully unmanned deployment under every weather condition
└─ Large-scale overseas delivery infrastructure

Capability 1: Low-temperature startup and thermal management
├─ Can the system start under low-temperature conditions?
├─ Does it have a thermal-control loop?
└─ Is long-duration thermal balance controllable?

Capability 2: Traction and chassis mobility
├─ Traction retention on snow / low-adhesion surfaces
├─ Anti-sink / anti-slip / correction capability
└─ Stable operation on typical facility roads and slopes

Capability 3: Snow-removal execution
├─ Handling of powder snow, wet snow, and locally compacted snow
├─ Identification of clogging risk in work mechanisms
└─ Repeatable basic clearing effect

Capability 4: Perception and safety
├─ Safety strategy under low visibility, icing, and occlusion
├─ Obstacle detection and boundary control
└─ Safe stop or controlled operation after perception failure

Capability 5: Remote monitoring and degraded operation
├─ Status telemetry
├─ Log recording
├─ Fault alerts
├─ Fail-safe
└─ Fail-degraded operation

Priority entry sequence:
├─ Tier 1: High-reliability scenarios
│  ├─ Data centers
│  ├─ Medical campuses
│  ├─ Logistics parks
│  └─ Industrial facilities
│
└─ Tier 2: Standardized scenarios
   ├─ Commercial parks
   ├─ Educational institutions
   └─ Standardized property campuses

Common characteristics of initial customers:
├─ High night-time response requirement
├─ High cost of failure
├─ Mature budget
└─ High requirement for reliability and auditability

In parallel with engineering progress, the project has begun customer-demand validation.
Current priority counterparties include:
├─ Large property management companies
├─ Large enterprise facilities-management teams
├─ Industrial park and logistics park operators
└─ High-reliability customers that care about night-time response and auditability

Current market feedback concentrates on three points:
├─ Broad recognition of replacing manual snow removal with automation
├─ Reliability, predictability, and responsibility boundaries are the top concerns
└─ Some customers are willing to enter more specific pilot-condition discussions

Entry path:
Engineering prototype completion
└─> Pre-winter scenario demonstration
    └─> Pilot opportunity
        └─> LOI / pilot interest
            └─> Series A customer-validation foundation

Single-site deployment logic:
├─ Small site: 2–3 units
├─ Medium site: 3–6 units
└─ Large site: 6–10 units

Early validation will focus on three models:
├─ Model 1: Equipment sales
│  ├─ For large customers with in-house operations capacity
│  └─ Direct payment path
│
├─ Model 2: Leasing / RaaS
│  ├─ Better aligned with service-replacement logic
│  └─ Suitable for customers purchasing capability as OPEX
│
└─ Model 3: Integration with existing service providers
   ├─ Replace part of manual OPEX first
   └─ Reduce first-entry friction

This round does not force the final model.
It must validate three commercial questions:
├─ Who pays?
├─ Who accepts delivery?
└─ Who bears operational responsibility?

Barrier 1: Extreme cold × heavy-duty load × autonomy × long-duration operation
├─ Single modules can be copied
└─ Multi-system engineering complexity is hard to replicate

Barrier 2: Product definition from facilities-operations perspective
├─ Not a robot that merely runs
└─ An industrial-grade system that must complete the task

Barrier 3: Cross-disciplinary integration capability
├─ Mechanics and chassis
├─ Energy and thermal management
├─ Electric drive and control
├─ Perception and autonomy
├─ Remote operations
└─ Testing and validation system

Barrier 4: Capability accumulation from prototype to platform
├─ Low-temperature operation experience
├─ Long-duration work strategy
├─ Failure-mode database
├─ Modular maintenance system
└─ Remote-operations loop

Barrier 5: AI and autonomous decision-making capability
├─ Not a single-point algorithm, but system-level autonomy
├─ Covers perception judgment, task decision-making, anomaly handling, and safe degradation
├─ Core value is making correct decisions continuously under uncertainty
└─ The key dividing line from automated equipment to industrial-grade autonomous systems

This is not a demo-building team.
It is a closed-loop team built around industrial-grade autonomy under extreme environments.

The team’s core mission is not only to assemble a prototype.
It is to connect scenario definition, industrial resources, algorithmic autonomy, full-machine engineering, market conversion, and supply-chain coordination into an operational, verifiable, customer-site-ready system.

Role: Founder / System Architect / Project Lead
Core Positioning: System definer, path setter, resource integrator
Capability Tags: Industrial systems / energy systems / cross-border engineering / fundraising execution
Responsibilities in this round:
├─ System objective definition
├─ Scenario and customer-path judgment
├─ Team organization and resource coordination
├─ Financing pace and milestone control
└─ Overall project execution

Role: Industrial Advisor / Industry Entry and Resource Coordination
Core Background: Vice Chair of Tsinghua Embodied Intelligence Industry Association
Core Value: Connects industrial resources, policy resources, and real application scenarios
Responsibilities in this round:
├─ Industrial resource introduction
├─ High-value scenario judgment
├─ Potential customer and partner connection
└─ Industrialization-path advisory

Role: Chief Scientist / Algorithms and Embodied Intelligence Lead
Core Background: Former unicorn CEO; algorithm scientist
Core Value: Combines algorithm depth, product judgment, and company-building experience
Responsibilities in this round:
├─ Embodied-intelligence algorithm-path judgment
├─ Perception, decision-making, and autonomy planning
├─ World-model / behavior-model direction review
└─ Integration of algorithm capability with real robotic systems

Role: CTO / Full-Machine Engineering and System Implementation Lead
Core Background: Former Philips robotics lead
Core Value: Turns models, control, and hardware into real operating machines
Responsibilities in this round:
├─ Full-machine engineering implementation
├─ Prototype structure and platform engineering
├─ R&D-to-prototype translation
├─ Cost and structural optimization
└─ Engineering delivery coordination

Role: COO / Commercialization, Market, and Customer Conversion Lead
Core Background: Former market lead at a unicorn robotics company
Core Value: Understands To B customer language and converts technical capability into orders and pilots
Responsibilities in this round:
├─ Customer-demand validation
├─ To B commercial-path design
├─ Pilot-customer and channel development
├─ Market-feedback loop
└─ Commercialization coordination

Role: Co-Founder / Industrial Chain and Engineering Resource Coordination Lead
Core Positioning: Supply chain, external manufacturing resources, and engineering support
Core Value: Supports prototype-stage resource coordination and prepares for future small-batch and mass-production transition
Responsibilities in this round:
├─ Industrial resource coordination
├─ Supply-chain and external manufacturing support
├─ Engineering investment structure and capacity planning
└─ Prototype-to-production transition preparation

Target raise:
US$2 million, or approximately RMB 15 million equivalent.

This round funds prototype completion, system validation, and pilot-entry readiness.
It does not fund blind expansion.

Milestone 1: Operational engineering prototype
Milestone 2: Key capability validation under low-temperature and snow conditions
Milestone 3: Customer demonstration and pilot-entry qualification
Milestone 4: LOI / pilot-interest foundation for the next round

Core risk-control principle:
Do not chase full autonomy at the beginning.
First prove that the system can complete real tasks safely and repeatedly in defined scenarios.

A seed-stage project is worth investing in when five questions are answered:
├─ Is the market real?
├─ Is the spending mature?
├─ Is the technical path clear?
├─ Can the team deliver the prototype?
└─ Are the milestones verifiable?

This round is not about proving an infinitely large future.
It is about proving that we can use finite capital to build the first industrial-grade extreme-cold snow-removal robot that can actually work.

More importantly,
if this round succeeds, it will prove that ARCBOS is not a single-device project.
It will prove that ARCBOS has the capability to continue building toward industrial-grade autonomous systems under extreme environments.

The main line remains prototype validation.
But the investment value is not only the prototype itself.
It is the system capability and platform starting point behind the prototype.

SnowBot is the first entry point.
Extreme-environment industrial autonomy is the long-term direction.

This round is not buying short-term revenue.
It is buying the first real proof of system capability.

If SnowBot can establish engineering validity under extreme-cold conditions,
ARCBOS will have the opportunity to build from one high-barrier product into a broader industrial robotics platform for extreme environments.

ARCBOS
ENGINEERED FOR EXTREME CONDITIONS

Seed Round: US$2 million / approximately RMB 15 million equivalent
Use of capital: prototype completion, system validation, and pilot-entry readiness