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aurivus

AI-powered scan-to-BIM conversion

Neural network platform that automatically detects and classifies objects in 3D point clouds from laser scans to accelerate BIM modeling workflows.

Pricing
free tier, contact the vendor
Alternatives
VIKTOR.AI, One Click LCA, GENx Design & Technology
Who it's for
Architecture, Structural Engineering, MEP Engineering
$1.02M raisedGrowth Stage
Founded 2019 · 2,000+ users in 56 countries (including industrial facilities and buildings)
Website → ← All Tools
WHY IT EXISTS

Why This Tool Exists

The Problem

BIM modelers spend 25-50% of their time manually identifying and tracing objects in point cloud data from building scans, leading to slow project delivery and increased modeling costs. Complex objects like MEP systems are particularly time-consuming to model accurately.

The Solution

Neural network automatically detects and classifies structural elements, MEP systems, architectural features, and furniture in point clouds, then groups object points for direct Revit modeling. Reduces modeling time by 25-50% with AI-powered object recognition.

USAGE

How You Use It

Delivery Method
SaaSPlugin
Integrations
Autodesk RevitE57 exportReCap RCP
Disciplines
Architecture, Structural Engineering, Mep Engineering, Construction
Workflows
Data & Automation
Project Phases
Design Development, Construction Documentation, Construction Administration
Project Types
Commercial, Industrial, Infrastructure, Institutional
TRANSPARENCY

Data Transparency

Exactly what this tool uses and how

Input
What it needs
Required:3D point cloud data
Optional:2D vector graphics, BIM model for comparison
Formats:E57, ReCap RCP, SLAM scanner data, Terrestrial laser scanner data, LiDAR, iPad Pro/iPhone scanner data
Output
What you get
Format:.aurivus files for Revit plugin and E57 classified point clouds
Fields:Detected object classes, Grouped point clusters by object, Structural elements (walls, floors, roofs), MEP systems (pipes, valves, fittings), Architectural features (windows, doors), Furniture and interior objects
Algorithm
How it works
Model:Proprietary neural network trained for building object detection (specific architecture not disclosed)
Accuracy:Claims significant time savings (25-50% modeling time reduction) but specific accuracy metrics not published
Privacy
How your data is protected
Retention:Personal data retained for contractual obligations, reviewed every 2 years
Training:Not publicly specified whether point cloud data used for AI training
Compliance:GDPR, Swiss Data Protection Act (DSG)
API
Integration
Endpoint:Contact vendor for API access
Method:Not publicly documented
USE CASES

Use Cases

  • ·Convert building laser scans to Revit BIM models
  • ·Automated MEP system detection and modeling from point clouds
  • ·Industrial facility documentation and reverse engineering
  • ·Structural element identification for renovation projects
  • ·Quality control verification of as-built conditions against design
PRICING

Pricing

Free
University/student access available
Pro
Contact vendor for pricing
Enterprise
Custom project-based pricing available
Sources & Research NotesResearch date: 2026-02-01
Data Sources (7)
Fields Checked (9)
problem, solution, deliveryMethod, integrations, customerBase, yearFounded, fundingTotal, privacy, algorithm-overview
Not Found (5)
detailed-algorithm-specs, accuracy-metrics, specific-api-documentation, detailed-pricing-tiers, point-cloud-training-data-policy
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.
ALTERNATIVES

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