📱

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 raised 🚀 Early Stage

Founded 2018 • Freelancers, small/mid-size businesses, enterprises (specific case: K12 school scanned in 16 scans)

Website → ← All Tools

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.

How You Use It

🚀

Delivery Method

Mobile App SaaS

🔌

Integrations

Revit AutoCAD SketchUp Rhino FBX DFX OBJ GLTF

Disciplines

Architecture, Construction, Facility Management, Owner/Developer

Project Phases

Pre-Design, Construction Documentation, Operations/FM

Project Types

Commercial, Residential, Education, Hospitality, Healthcare

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

• 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

Free

Free trial available (no credit card required)

Pro

Starts at $19 (one-time license model)

Enterprise

Contact for enterprise/volume licensing

Website →

📚 Research Sources & Data Quality Last verified: 2026-02-01

Data Sources (10)

✅

Verified Data (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

Our Commitment: We only include verified data from official sources. If information isn't publicly available, we mark it as "Not publicly specified" rather than guessing.