Bessemer leads $10M seed for MeltPlan's preconstruction AI planning engine

MeltPlan Raises $10 Mn To Accelerate Its AI Planning Engine For Preconstruction

AI-native preconstruction startup MeltPlan has secured $10 Mn (about ₹90.9 Cr) in seed funding led by Bessemer Venture Partners, with participation from European early-stage tech investment firm Nova. Founded in 2025 by ex-Innovacer CPO Kanav Hasija and former DPR Construction executive Tanmaya Kala, the company builds AI agents that understand building codes, materials, sequencing, procurement, and construction methods. MeltPlan's total funding now stands at $14 Mn. Early enterprise users include DPR Construction (California) and Innovo Group (UAE).

The Product: A Four-System Planning Engine

MeltPlan is building a planning engine made up of four integrated systems: code, cost, schedule, and value. The goal is simple: simulate outcomes before plans are locked, so teams avoid expensive redesigns and rework.

"We're building an AI system that allows teams to evaluate constraints, run scenarios, and align before plans are frozen," said Hasija. For product teams, this is a decision-support layer that reduces uncertainty at the top of the construction funnel-where choices are cheapest to change and most expensive to get wrong later.

How It Likely Works (From a Product Lens)

• Inputs: BIM models, specs, historical cost data, vendor catalogs, lead times, local building codes, site constraints, and schedule assumptions.
• Reasoning: AI agents map constraints (code, budget, logistics), propose alternatives, and quantify trade-offs across cost, time, and value.
• Outputs: Scenario comparisons, compliance checks, risk flags, procurement plans, and program-level recommendations.
• Interfaces: Human-in-the-loop reviews, versioned decisions, and integrations with BIM, estimating, and scheduling tools.

What This Means For Product Development Teams

• Clear JTBD: "Freeze better plans, faster." Reduce change orders, compress bid cycles, and surface issues before they become field problems.
• Decision quality over data volume: Make scenarios explainable and auditable. Attach a reason code to every recommendation.
• Trust by design: Verify code citations, show cost baselines, and expose assumptions behind each schedule path.
• Workflow-native UX: Meet users inside existing takeoff, estimating, and scheduling tools. Fewer tabs, tighter handoffs.

Key Product Questions MeltPlan Will Need To Solve Next

• Local code coverage and updates: How are jurisdiction changes sourced, validated, and versioned?
• BIM interoperability: Smooth parsing across Revit, IFC, and contractor-specific standards without brittle mappings.
• Procurement volatility: Dynamic pricing, supplier reliability, and lead-time modeling baked into scenarios.
• Explainability: Show the chain of reasoning from code requirement to design constraint to cost/schedule impact.
• Human-in-the-loop: Fast redlines, role-based approvals, and reversible decisions with audit trails.
• Security and data residency: Enterprise-grade controls for plans, bids, and vendor data.
• KPIs that matter: Change-order reduction, variance against baseline, time-to-bid, and win rate uplift.

Go-To-Market Signals And Wedges

• Anchor customers: Early traction with DPR Construction and Innovo Group shows enterprise fit.
• Wedge feature: Automated code compliance and "what-if" cost/schedule scenarios can earn the first seat before expanding to value engineering and procurement planning.
• Proof of ROI: Benchmark against historical projects: fewer RFIs, faster approvals, more accurate bids.
• Partner ecosystem: Integrations with BIM, estimating, scheduling, and supplier data networks to reduce switching costs.

Practical Takeaways For Product Leaders

• Ship modularly: Start with code + cost, then layer schedule and value once trust is earned.
• Design for decisions: Every screen should shorten the path to "approve, revise, or compare."
• Make the invisible visible: Surface constraints early (code, lead times, site logistics) and quantify their impact.
• Own the handoff: Turn preconstruction decisions into structured outputs consumable by field ops and procurement.
• Close the loop: Feed as-built outcomes back into models to improve future recommendations.

If you build products in this space, explore AI for Product Development and AI for Real Estate & Construction for tactics on modeling, validation, and rollout in construction workflows.