AI platform MeltPlan raises $10 million in Seed funding led by Bessemer Venture Partners to make construction boring
MeltPlan, a pre-construction AI platform founded in 2025 announced its $10 Mn Seed funding led by Bessemer Venture Partners, with participation from noa. This brings the total funds raised by the company to $14 Mn. The funds will be used to further develop what it calls the “planning engine” for the $14 Tn construction industry, an AI system designed specifically to optimize decisions before construction begins. The funding will accelerate MeltPlan’s product development across code, cost, schedule, and value decision systems.
While design software optimizes use and aesthetics and construction software optimizes execution and control, MeltPlan is building the missing layer - software that optimizes decisions and tradeoffs upstream, before scope is locked, procurement begins, and change orders become inevitable.
“Construction doesn’t fail because teams are not skilled or execute poorly,” said Kanav Hasija, Co-founder and CEO of MeltPlan. “It fails because preconstruction teams are fragmented and commit too early with incomplete information. We’re building an AI system that allows teams to evaluate constraints, run scenarios, and align before plans are frozen.”
Kanav, who previously co-founded a $3 Bn health tech startup Innovaccer to make healthcare in the US more affordable and accessible, is now focusing on making the construction industry more productive. He is joined by Tanmaya Kala, Co-founder and COO, a Stanford educated civil engineer who was project executive at DPR Construction handling large commercial, healthcare, and life science projects.
MeltPlan’s long-term goal is to help teams make construction “boring” by making planning more intense: surfacing constraints and tradeoffs early, aligning stakeholders before plans are frozen, and reducing the need for late-stage redlines, rework, and change orders. “Construction should be boring,” said Hasija. “Planning should be intense. If your construction phase is stressful, something went wrong earlier.”
“Built environment workflows are full of irreversible decisions made under uncertainty,” said Pankaj Mitra, Partner at Bessemer Venture Partners. “MeltPlan is approaching preconstruction as a system, not a phase, building a visionary ‘planning engine’ layer that helps teams quantify tradeoffs early and reduce downstream volatility.”
MeltPlan is working with top enterprise contractors like DPR Construction in California and Innovo Group in UAE to help in their planning or preconstruction phase of construction.
An AI system designed for construction, not adapted to It
Unlike general-purpose AI tools adapted for construction, MeltPlan is building a construction-native AI system that is designed to understand building codes, materials, sequencing, procurement, and construction methods. The system has already scored 95% or higher on building inspector exams, and the company is working to expand its expertise across all trades and disciplines in construction planning.
The Planning Engine will have four integrated systems:
- Code System: Context-aware compliance pathways for architects, engineers, and inspectors
- Cost System: Risk-oriented takeoffs / quantity survey, bid scoping, and leveling for general contractor and trade contractor preconstruction teams
- Schedule System: Scenario-based sequencing and what-if planning before scope freeze
- Value System: Value impact analysis and optimization recommendations for owners and developers
Rather than digitizing existing workflows, MeltPlan aims to help teams simulate outcomes before committing to them.
“Preconstruction is treated like a phase,” Kala said. “But it’s actually the operating system of the project. When decisions evolve upstream, execution downstream becomes boring - and that’s a good thing.”
Customer testimonial
“As responsible contractors, we always believe in investing more time in preconstruction,” said Atul Khanzode, DPR Construction Leadership Team Member. “We are excited to partner with MeltPlan to help us reduce planning errors while speeding up the preconstruction process”.
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