Helonic is an AI construction drawing analysis platform for insurance underwriters who review 2D PDF drawing sets.
Assess project risk, quantify defect potential, and make data-driven underwriting decisions directly from construction drawing analysis, before coverage is bound.
Underwriters price construction risk without a sheet-level record of the design issues that later become defect claims.
Construction defect claims are among the most costly in commercial insurance, but underwriters lack objective tools to evaluate design quality before binding coverage.
Without technical drawing analysis, underwriters rely on project size, cost, and team reputation, missing the design quality signals that predict claims.
Pricing construction risk accurately requires understanding the specific technical risks in each project's design, not just aggregate industry data.
Helonic produces a project-level drawing-risk summary that underwriters can file beside the application and reuse across the book.
Analyze construction documents before coverage is bound to generate a risk score based on design quality, coordination completeness, and code compliance, giving underwriters objective data for pricing decisions.
Identify high-risk coordination areas, common defect patterns, and design quality indicators that correlate with future claims based on analysis of the actual construction documents.
Screen drawings against building code, fire code, and accessibility requirements to identify compliance gaps that could lead to code violation claims or remediation costs.
Compare drawing quality metrics against industry benchmarks to understand where a project stands relative to similar building types, sizes, and complexity levels.
Analyze drawing sets across your insured portfolio to identify systemic risk patterns, common deficiencies, and opportunities to improve loss ratios through targeted risk mitigation.
Helonic flags missing waterproofing details, rating gaps, and coordination density that historically track with defect claims.
Underwriters can price and select accounts from documented drawing quality instead of a narrative risk description.
Milind is the co-founder and CEO of Helonic, where he leads product and go-to-market for AI-powered construction drawing analysis. He works closely with general contractors, project managers, estimators, and owners to understand how drawing quality drives project outcomes - and where AI can reduce RFIs, change orders, and rework. Milind has interviewed hundreds of construction professionals across project delivery roles, from preconstruction estimators at ENR top-400 contractors to facilities directors at institutional owners, and uses those conversations to shape both product direction and the way Helonic talks about the work.
How this page was researched: Guidance references the drawing-quality signals that correlate with claim risk, grounded in Helonic's review of sets across project types and the industry rework and defect data. Examples reflect the patterns that distinguish high-risk from low-risk documentation.
Last reviewed by Milind Sagaram · August 4, 2026
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