Gunjo · Business Intelligence for the AI Era
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AI-Driven Construction Cost Precision Control SaaS

1) Traditional cost software licenses and annual subscriptions; cloud-based value-added services for AI cost calculation

MODEL

Key Fields

FIELD STAMPS
IndustrySaaS / Enterprise Software
RegionChina
ScaleGiant
ChannelHybrid

📌 Background

In 2026, Glodon moves into a dual-wheel phase of digital construction and AI-driven operations, shifting from selling software to providing data-driven cost precision services. According to the company's 2025 annual report, full-year operating revenue reached 6.068 billion yuan, with net profit attributable to shareholders of 405 million yuan, a year-on-year increase of 61.77%. Among this, digital cost business revenue was 4.779 billion yuan, accounting for nearly 80%, and the gross profit margin rose to 58.33%, an increase of over 22 percentage points compared to the previous year.

👤 Target Customers

General construction contractors, project owners, cost consulting agencies, and government-funded project entities

💰 Revenue Streams

1) Traditional cost software licenses and annual subscriptions; cloud-based value-added services for AI cost calculation and change early warning billed by project scale; BIM data collaboration platform seat subscription fees; 2) Cost training: structured hands-on quantity takeoff training and advanced feature courses charged per term for construction enterprise cost teams; 3) Private deployment: lump-sum implementation fees for localized deployment and integration with existing cost systems for large construction enterprises and government-funded project owners.

🧮 Cost Structure

AI R&D and computing power investments, BIM engine and lightweight platform maintenance, and nationwide sales and implementation network costs

🛡️ Moat

AI training corpus barriers formed by two decades of accumulated construction cost industry data, switching costs for design institutes and general contractor clients, and a nationwide implementation service network

🔑 Keys to Success

  • Deep integration of AI models with industry cost data
  • Transitioning from a tool-based to a platform-based subscription model
  • Scaling up after validating return on investment through pilot benchmark projects

⚠️ Risks

  • Construction industry cyclical fluctuations compressing client budgets
  • AI product commercialization progress falling short of expectations

🏢 Cases

  • Glodon

📊 SWOT Analysis

Strengths

  • Leading market share in domestic cost software with a massive client base
  • Deep synergy between AI and existing cost data creating differentiation

Weaknesses

  • Revenue under pressure during cyclical downturns with heavy transformation investments
  • AI commercialization monetization pace and profitability still awaiting validation

Opportunities

  • Affordable housing and urban renewal projects releasing incremental demand
  • AI data-driven cost precision providing new pricing space for cost consulting upgrades

Threats

  • Open-source and cloud-based competitors eroding the long tail amid domestic substitution
  • Real estate and infrastructure cyclical fluctuations transmitting to SaaS renewal rates