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Schematic Energy Analysis Workspace

Supports architects and interior design teams with integrated lighting analysis, sustainable material selection, generative massing optimization, and faster visualization workflows to improve energy efficiency and design iteration during design development.

The Problem

“Sustainable Design Energy Analysis Copilot for Architecture and Interior Design”

Organizations face these key challenges:

1

Lighting analysis often requires rebuilding or exporting geometry into separate tools

2

Material selection involves manual comparison across sustainability criteria, cost, aesthetics, and performance

3

Early-stage massing exploration is constrained by time, human bandwidth, and complex zoning/solar/daylight tradeoffs

4

Rendering workflows are slow and expensive when many client-facing iterations are required

Impact When Solved

Reduce lighting validation turnaround by keeping analysis inside Revit-connected workflowsImprove sustainable material decisions with interactive multi-criteria recommendationsGenerate and rank more massing options under zoning, daylight, solar, and facade constraintsAccelerate rendering and visualization iteration for client reviews

The Shift

Before AI~85% Manual

Human Does

  • •Export BIM geometry and rebuild models in separate lighting and energy analysis tools
  • •Compare material datasheets, certifications, cost, and performance in spreadsheets
  • •Manually create and review massing options against zoning, daylight, and solar constraints
  • •Coordinate design revisions and resolve version mismatches across architecture, lighting, and visualization workflows

Automation

    With AI~75% Automated

    Human Does

    • •Set project priorities, design intent, and tradeoff weights for energy, aesthetics, cost, and sustainability
    • •Review and approve recommended materials, massing options, lighting changes, and client-facing visuals
    • •Handle exceptions, code interpretation, and final decisions when recommendations conflict with project goals

    AI Handles

    • •Run Revit-connected lighting analysis and return illuminance results, compliance checks, and design recommendations
    • •Rank material options against sustainability, cost, performance, and aesthetic criteria with explainable tradeoffs
    • •Generate and score feasible massing options under zoning, parcel, daylight, solar, facade, and program constraints
    • •Accelerate rendering and visualization iterations for faster design reviews and client feedback incorporation

    Operating Intelligence

    How Schematic Energy Analysis Workspace runs once it is live

    AI runs the first three steps autonomously.

    Humans own every decision.

    The system gets smarter each cycle.

    Confidence94%
    ArchetypeRecommend & Decide
    Shape6-step converge
    Human gates1
    Autonomy
    67%AI controls 4 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapeconverge

    Step 1

    Assemble Context

    Step 2

    Analyze

    Step 3

    Recommend

    Step 4

    Human Decision

    Step 5

    Execute

    Step 6

    Feedback

    AI lead

    Autonomous execution

    1AI
    2AI
    3AI
    5AI
    gate

    Human lead

    Approval, override, feedback

    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

    Real-World Use Cases

    Integrated lighting analysis inside Revit

    It checks a building model and predicts how bright different rooms and surfaces will be before anything is built.

    Physics-based simulation and design analysis rather than generative AI or predictive ML.mature commercial workflow packaged as a revit add-in.
    10.0

    AI-assisted generative building massing and sustainability optimization for SolVista

    The team gives the software the building rules and goals, then it creates many possible building designs and helps pick the few that best balance daylight, facade glazing, and rooftop solar potential.

    constraint-based generative search with multi-objective evaluation and human-in-the-loop filteringdeployed workflow in a real project case study, with human-guided decision making rather than full automation.
    10.0

    AI-assisted architectural rendering and visualization at KPF using D5 Render

    KPF uses AI tools in D5 Render to turn building designs into realistic images much faster, so architects can see and improve ideas in hours instead of weeks.

    Generative visual enhancement and context-aware image transformation for architectural visualizationdeployed production workflow at a major architecture firm, though described in a sponsored case study.
    10.0

    Interactive AI-assisted material selection for sustainable building design

    An AI helper suggests building materials that better fit sustainability goals, making it easier for designers to compare options without manually checking every product.

    Decision support and recommendationresearch-stage proposed workflow
    9.5

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