The National Highways Authority of India (NHAI) has deployed an in-house artificial intelligence tool to catch costly errors in detailed project reports (DPRs) before construction begins, aiming to prevent the kind of planning oversights that can balloon a ₹2,000-crore project budget by ₹200 crore to ₹300 crore. For real estate and construction professionals, DPRs are the contractual and technical foundation of any large infrastructure job - so getting them right from the start directly controls project margins and delivery timelines.
Catching gaps before ground is broken
The software, called the Technical Schedule Analyzer, compares quantities and specifications in a DPR's Schedule B and Schedule C sections against Indian Roads Congress (IRC) codes, Ministry of Road Transport and Highways norms, and NHAI's own standards. It flags inconsistencies, missing items, and deviations from policy before the project moves to execution. NHAI built the tool by assembling a multidisciplinary team of civil engineers, AI engineers, and design specialists, who digitized thousands of circulars, manuals, and engineering documents into a structured knowledge repository.
"Detailed Project Reports are our biggest problem. In a ₹2,000-crore project, variations of ₹200 crore to ₹300 crore can arise simply because something was missed during planning. This tool helps identify those gaps at the DPR stage itself," NHAI chairman Santosh Kumar Yadav said.
Margsarthi: A closed knowledge system for engineers
The effort has grown into a broader AI ecosystem centred on Margsarthi, NHAI's in-house AI assistant. Unlike public platforms that pull information from the open internet, Margsarthi operates inside a closed environment, trained exclusively on NHAI circulars, Acts, IRC codes, and other vetted technical documents. Launched in April, the chatbot has already handled more than 50,000 queries from around 1,100 users across the organization. Every response links back to its source document, so engineers can verify the basis of any recommendation.
One of its most frequent uses is resolving on-site technical disagreements. Previously, contractors and authority engineers would spend hours searching through manuals when they interpreted a provision differently. Now they can query the system, which points them to the exact IRC code or policy provision. "Once everyone is looking at the same rule, disagreements are resolved much faster," Yadav said.
Real-world applications and future tools
The platform is also being used to analyze lengthy legal and arbitration records, cutting review time from hours to minutes. In another example, Yadav uploaded a field photograph of an improperly cut slope, and the system immediately identified the deficiency and suggested corrective measures based on applicable standards. NHAI is now developing AI tools that will continuously scan project data for delays, quality defects, and emerging risks. Future applications include automated road defect analysis, AI-assisted drawing reviews, signage planning, and support for environmental and forest clearance applications.
Officials stressed that these tools are decision-support systems, not replacements for human judgment. Project directors and engineers remain accountable for all decisions, with AI-generated insights serving as one input among many.
Why this matters for Real Estate & Construction
NHAI's deployment shows how AI can systematically reduce the contractual and financial risks that start with flawed planning documents. For professionals in the broader building and infrastructure sector, the same approach - using AI to check design specifications against building codes, zoning rules, and cost databases - could catch similar gaps in residential, commercial, and mixed-use projects. As the industry explores AI for Real Estate & Construction, automated plan review and specification auditing are becoming practical tools for controlling pre-construction risk, not just in highways but in any large-scale development where a missed detail can trigger claims, change orders, and significant schedule delays.
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