Detailed Problem StatementBuilding energy assessments form the backbone of global decarbonization efforts, yet current methodologies remain fragmented and inefficient. Across surveyed countries, assessment timelines range from 24 hours (simplified US systems) to 3 weeks (comprehensive German Bedarfsausweis), creating market bottlenecks that delay property transactions and retrofit programs. With the EU requiring all buildings to achieve minimum energy performance standards by 2030 and similar mandates emerging globally, the current infrastructure cannot scale to meet demand.Manual data collection accounts for 60% of assessment time across all studied systems. Assessors spend considerable time measuring floor areas, documenting insulation specifications, and inputting data into proprietary software platforms. Error rates in manual data entry average 12-15%, requiring costly re-assessments. Limited assessor availability creates further constraints, with wait times extending to 8 weeks in peak periods across several markets.ObjectivesDrive innovation in AI to address inefficiencies in current assessment processes.Support compliance with diverse international energy standards and policies.Desired OutcomesTimeline Reduction: From current 1-6 weeks to under 24 hoursAccuracy Target: 95% correlation with certified manual assessmentsCost Reduction: 60% decrease in assessment costs through automationMarket Adoption: 50,000 assessments within 18 months across three initial marketsAssessor Productivity: 5x increase in assessments per assessor through AI augmentationData Quality: Error rates reduced from 12-15% to under 3%