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OpalAi - ScanTo3D & Floorplan

Mobile LiDAR to BIM conversion with AI floor plans

AI-powered mobile scanning app that transforms iPhone/iPad LiDAR data into accurate 3D models, floor plans, and BIM files instantly using computer vision and machine learning algorithms.

$856K raisedEarly Stage
Founded 2018 · Freelancers, small/mid-size businesses, enterprises (specific case: K12 school scanned in 16 scans)
Website →← All Tools
WHY IT EXISTS

Why This Tool Exists

The Problem

Traditional floor plan creation requires manual site measurement and specialized surveyors, taking weeks and costing thousands of dollars. Teams using manual methods struggle with accuracy inconsistencies, time delays, and high labor costs for documentation needed in appraisals, insurance, leasing, and facility management.

The Solution

OpalAi uses iPhone/iPad LiDAR sensors and proprietary AI to generate accurate 2D floor plans and 3D BIM models in minutes. Users perform a simple walkthrough scan, and the AI algorithm instantly creates appraisal-quality floor plans with <1 inch error per 30 feet, exportable to Revit, AutoCAD, SketchUp, and other formats.

USAGE

How You Use It

Delivery Method
Mobile AppSaaS
Integrations
RevitAutoCADSketchUpRhinoFBXDFXOBJGLTF
Disciplines
Architecture, Construction, Facility Management
Workflows
Design & BIM, Operations & Facility Management
Project Phases
Pre-Design, Construction Documentation, Operations/FM
Project Types
Commercial, Residential, Education, Hospitality, Healthcare
TRANSPARENCY

Data Transparency

Exactly what this tool uses and how

Input
What it needs
Required:iPhone/iPad with LiDAR (iPhone 13 Pro+ or iPad Pro 2020+), Target space or building
Optional:Custom naming conventions, GLA calculations, Multiple room stitching
Formats:LiDAR point cloud data (automatic capture via mobile device)
Output
What you get
Format:Floor plans, 3D models, and BIM files in multiple formats
Fields:ANSI-compliant floor plans, Gross living area (GLA) calculations, 3D CAD models, BIM objects, Texture and color mapping, Point cloud data (OBJ, GLTF, GLB, STL formats)
Algorithm
How it works
Model:Proprietary computer vision and machine learning on LiDAR point cloud data
Accuracy:98% accuracy with error <1 inch per 30 feet (claimed; methodology not publicly detailed)
Privacy
How your data is protected
Retention:Project-based retention policy (specific duration not publicly disclosed)
Training:Not publicly specified
Compliance:Not publicly specified (SOC 2 status unknown)
API
Integration
Endpoint:Contact vendor for API access
Method:Not publicly documented
USE CASES

Use Cases

  • ·Real estate appraisal documentation and measurement
  • ·Insurance property claims and inspections
  • ·Facility management and space planning
  • ·Lease and building documentation
  • ·Retrofit and renovation planning from accurate existing conditions
  • ·Fast-track BIM model creation for design teams
PRICING

Pricing

Free
Free trial available (no credit card required)
Pro
Starts at $19 (one-time license model)
Enterprise
Contact for enterprise/volume licensing
Sources & Research NotesResearch date: 2026-02-01
Data Sources (10)
Fields Checked (21)
vendor, website, fundingTotal, yearFounded, maturityStage, problem, solution, deliveryMethod, integrations, disciplines, projectPhases, projectTypes, input-required, input-formats, output-format, output-fields, algorithm-approach, algorithm-accuracy, use-cases, pricing-free, pricing-pro
Not Found (8)
api-documentation, compliance-certifications, privacy-data-retention-exact-period, privacy-training-data-policy, detailed-customer-list, soc2-certification, gdpr-compliance-details, api-endpoint-documentation
About this research: This record summarizes the cited sources and fields checked. A research date does not establish hands-on testing. Consult the original sources for current details and limitations.
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