H2 Corporation Raises Pre-Series A Extension Funding Led by NEA

H2 Corporation, which provides the “AI Estimation” platform for estimating work automation, has raised funds in a pre-series A extension round from venture capital firms including New Enterprise Associates (NEA) of the United States.

H2Corporation’s latest funding round was led by New Enterprise Associates (NEA), a global venture capital firm with $28 billion in assets under management, with participation from existing investors Spiral Capital, JAFCO, MetaProp, One Capital, and Boost Capital.

We possess our own domain-specific AI and provide not just an estimation automation tool, but an AI-native platform system (System of Record) that integrates all functions important to companies and is customizable. NEA, which has previously backed technology giants such as Salesforce, Databricks, and Cloudflare, joining H2 is a strong vote of confidence in our technological capabilities and global potential.

Also Read: Notta Raises $23M Series B Backed by Granite-Integral

We are convinced that as a Tokyo-based startup, we have the technological capabilities to compete on equal terms or better with the world’s leading companies, overturning the conventional wisdom that “disruptive innovation only comes from Silicon Valley.”

The construction industry is one of the largest in the global economy, yet one of the least digitalized and automated. For decades, “innovation” in this industry has meant little more than the introduction of slightly improved spreadsheets, clunky legacy software, or rigid SaaS products that don’t scale to the realities of the jobsite.

Our flagship product, AISekisan Platform (AISP), is the result of collaboration with the industry’s largest building materials and components manufacturers, distributors, general contractors, and specialized construction companies. AISP is a “System of Record” infrastructure system for construction industry estimating and order placement. By utilizing AISP, customers can process hundreds of thousands of estimating and order placement tasks, enabling them to move away from legacy systems significantly faster and more cost-effectively than traditional systems.

SOURCE: PRTimes