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BAMROC

AI-powered automatic MEP and structural clash resolution

AI-powered Revit plugin that automatically detects and resolves clashes between mechanical, electrical, plumbing (MEP) systems and structural elements in BIM models. Uses Movement and Bend resolution strategies to optimize geometry while respecting design intent and constructability constraints.

Not publicly specified (pre-funding) raisedEarly Stage
Founded 2023 · Less than 5 customers (beta/early deployment phase). Known project: 1M sq.ft. hospital with 1,100+ resolved clashes.
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WHY IT EXISTS

Why This Tool Exists

The Problem

MEP coordinators manually identify and resolve 100+ clashes between mechanical, electrical, and plumbing systems and structural elements, spending 40+ hours per model on repetitive coordination work. Manual resolution delays pre-construction timelines and introduces rework when conflicts are discovered later in the construction phase.

The Solution

Automatically detects MEP vs. MEP and MEP vs. Structure clashes using AI, then applies optimized resolution strategies (Movement or Bend) to resolve conflicts in minutes instead of hours. Achieves 94% resolution rate and claims 11x faster throughput with 67% cost reduction versus manual coordination.

USAGE

How You Use It

Delivery Method
SaaSRevit PluginCloud-Based
Integrations
RevitNavisworksBIM360/ACCAutodesk Construction Cloud
Disciplines
Mep Engineering, Architecture, Construction
Workflows
Coordination & Delivery
Project Phases
Design Development, Construction Documentation, Construction Administration
Project Types
Commercial, Residential, Healthcare, Infrastructure
TRANSPARENCY

Data Transparency

Exactly what this tool uses and how

Input
What it needs
Required:Revit BIM model, MEP systems, Structural elements
Optional:Custom clearance distances, Design constraints, Priority rules
Formats:RVT (Revit), IFC (roadmap)
Output
What you get
Format:Resolved BIM model with clash resolution validation reports
Fields:Clash locations and geometry, Resolution strategy applied (Movement or Bend), Adjusted element positions and properties, Clash summary statistics and success rate, Design impact assessment, Before/after clash visualizations
Algorithm
How it works
Model:Proprietary AI system (model architecture not publicly disclosed)
Accuracy:94% successful clash resolution rate (demonstrated in 1M sq.ft. hospital beta project with 1,100+ clashes)
Privacy
How your data is protected
Retention:Not publicly specified
Training:Not publicly specified
Compliance:Not publicly specified
API
Integration
Endpoint:Contact vendor for API access
Method:Not publicly documented
USE CASES

Use Cases

  • ·Automatic detection and resolution of MEP vs. structural clashes during design coordination
  • ·Pre-construction clash coordination to prevent field conflicts and rework
  • ·Real-time BIM model coordination with instant clash feedback to multidisciplinary teams
  • ·Design optimization and material waste reduction through automated clearance management
  • ·Large-scale coordination on complex projects (hospital, mixed-use, infrastructure)
  • ·Quality assurance on coordinated BIM models before construction documentation release
PRICING

Pricing

Free
Free trial available (no credit card required)
Pro
Not publicly specified
Enterprise
Contact for custom pricing
Sources & Research NotesResearch date: 2026-04-03
Data Sources (10)
Fields Checked (17)
problem, solution, deliveryMethod, integrations, disciplines, projectPhases, projectTypes, yearFounded, maturityStage, vendor, website, algorithm-approach, algorithm-accuracy, useCases, input-formats, output-structure, control-features
Not Found (10)
fundingTotal, customerBase-verified-names, api-documentation, algorithm-model-details, privacy-policy, data-retention-policy, compliance-certifications, detailed-pricing-plans, soc2-certification, iso27001
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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