Publicly traded construction management software focused on execution and field ops, not pre-construction decision intelligence.
Venture-backed preconstruction cost management platform that is the closest direct competitor, though Join is cost-first while MeltPlan is decisions-and-tradeoffs-first.
AI-driven construction scheduling and simulation, overlapping on optimization but positioned post-scope-lock rather than upstream of it.
A founder team with rare combined depth in vertical AI deployment and on-the-ground construction operations, building agentic software for a decision layer that incumbent execution-focused platforms have structural reasons not to enter.
MeltPlan runs agentic planning models that ingest project inputs, identify constraints, surface stakeholder tradeoffs, and produce structured pre-construction artifacts before scope decisions harden into expensive change orders.
Gives architecture firms an AI teammate for specs, compliance, and documentation.
Architecture firms spend more time on specs, compliance, and documentation than on design. Avoice already manages $300M+ in active projects across five countries, proving the workflow sticks in an industry that resists new software.
Helps electrical contractors win more jobs faster by automating estimating and proposals with AI.
Data center construction is booming and electrical contractors are the bottleneck. Bidflow automates the estimating and bidding paperwork that keeps them from taking more jobs, starting with a vertical no general-purpose tool covers well.
Centralizes energy data into an AI-powered OS for cheaper, more reliable electricity at scale.
Large commercial and industrial electricity buyers manage procurement, consumption, and sustainability across disconnected spreadsheets and broker relationships. Condor centralizes it into an AI-powered Energy OS, targeting data centers as the fastest-growing demand segment.
Creates digital twins of buildings and data centers to autonomously optimize energy and cooling.
Traditional CFD simulations for building energy optimization take hours. Inviscid's physics-informed neural networks run in real time, producing physically accurate results that pure ML approaches cannot guarantee. The founders have PhD-level expertise in the exact technique.