Key Takeaways
- • Bessemer Venture Partners co-led a $34M Series A for Mach9, an AI-native CAD software company automating civil engineering survey deliverables from LiDAR scans.
- • Mach9 ingests LiDAR scans and imagery, uses AI to extract features a surveyor would otherwise draw manually, and has the surveyor edit and refine the model's first draft.
- • Pricing is usage-based, per mile of road or acre surveyed, so Mach9 monetizes the project labor budget rather than competing for IT software spend, according to Bessemer.
- • Co-founders Alexander Baikovitz (CEO), Haowen Shi, Michael Mong, and Zachary Sussman met at Carnegie Mellon while building robots.
Summary
Bessemer Venture Partners co-led a $34M Series A round for Mach9, a company building AI-native CAD software for civil engineers. According to Bessemer, the investment targets a bottleneck in infrastructure work: field crews capture the physical world with LiDAR, but drafters still manually trace curbs, lines, poles, and drainage structures in desktop software that Bessemer describes as decades old.
Bessemer frames the opportunity against a U.S. infrastructure buildout that it compares to the railroad expansion of the 19th century. Federal and state programs are funding repairs to roads and bridges, data centers are straining power and water systems, and utilities are upgrading the grid, with broadband and electrification adding further projects. Bessemer argues that capture technology has become cheaper and faster, but extraction has not kept pace, and that the shortage of engineers and drafters limits how much infrastructure can be mapped, designed, and maintained. Because surveys underpin design, permitting, construction, and asset management, errors and delays propagate through each subsequent stage.
Mach9 ingests LiDAR scans and imagery and uses AI to propose a first-draft set of extracted features, which the surveyor then edits. Bessemer says the product's accuracy bar is high because survey outputs feed directly into construction, and that customers now run the platform in production and tend to expand usage once it becomes part of project delivery.
Bessemer cites three factors behind its investment. First, Mach9 prices per mile of road or acre surveyed, drawing on project budgets rather than IT budgets, with revenue growing alongside customer work. Second, Bessemer argues that each project improves the models and that the platform becomes a system of record, creating a proprietary dataset of scan data paired with survey-grade deliverables, while incumbents sell seat-licensed tools. Third, Bessemer views accurate survey data as the foundation for civil engineering workflows.
The proceeds will fund expansion of Mach9's go-to-market team and product development across new data sources and feature types, with sales aimed at engineering firms, public agencies, and utilities. Bessemer, which also cites its backing of Procore, EvenUp, Legora, and Abridge, describes Mach9 as a vertical, AI-native product that makes skilled labor more productive.
Bessemer frames the opportunity against a U.S. infrastructure buildout that it compares to the railroad expansion of the 19th century. Federal and state programs are funding repairs to roads and bridges, data centers are straining power and water systems, and utilities are upgrading the grid, with broadband and electrification adding further projects. Bessemer argues that capture technology has become cheaper and faster, but extraction has not kept pace, and that the shortage of engineers and drafters limits how much infrastructure can be mapped, designed, and maintained. Because surveys underpin design, permitting, construction, and asset management, errors and delays propagate through each subsequent stage.
Mach9 ingests LiDAR scans and imagery and uses AI to propose a first-draft set of extracted features, which the surveyor then edits. Bessemer says the product's accuracy bar is high because survey outputs feed directly into construction, and that customers now run the platform in production and tend to expand usage once it becomes part of project delivery.
Bessemer cites three factors behind its investment. First, Mach9 prices per mile of road or acre surveyed, drawing on project budgets rather than IT budgets, with revenue growing alongside customer work. Second, Bessemer argues that each project improves the models and that the platform becomes a system of record, creating a proprietary dataset of scan data paired with survey-grade deliverables, while incumbents sell seat-licensed tools. Third, Bessemer views accurate survey data as the foundation for civil engineering workflows.
The proceeds will fund expansion of Mach9's go-to-market team and product development across new data sources and feature types, with sales aimed at engineering firms, public agencies, and utilities. Bessemer, which also cites its backing of Procore, EvenUp, Legora, and Abridge, describes Mach9 as a vertical, AI-native product that makes skilled labor more productive.
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