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SafeAI

Autonomous retrofit platform for heavy equipment

Multi-sensor autonomous retrofitting platform that converts existing construction and mining equipment into fully autonomous vehicles using LiDAR, radar, cameras, and AI.

$59M raisedGrowth Stage
Founded 2018 · MACA (100+ autonomous vehicles deployed), Obayashi Corporation (300+ vehicles retrofitted), Siemens partnership for zero-emission autonomous construction fleets
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WHY IT EXISTS

Why This Tool Exists

The Problem

Construction and mining companies must choose between purchasing expensive new autonomous equipment (hundreds of thousands of dollars per vehicle) or continuing with manual operation. Equipment downtime during retrofits costs operators $5,000+ per day in lost productivity.

The Solution

SafeAI retrofits existing heavy vehicles (dump trucks, haul trucks, dozers) with autonomous hardware (LiDAR, radar, cameras) and ASIL D-certified safety software. The retrofit approach preserves equipment assets, enables rapid deployment without vehicle replacement, and reduces operational costs while improving safety and productivity.

USAGE

How You Use It

Delivery Method
Hardware InstallationSaaS
Integrations
Not publicly specified
Disciplines
Construction, Civil Engineering
Workflows
Visualization & Presentation
Project Phases
Construction Administration, Operations/FM
Project Types
Commercial, Infrastructure, Industrial
TRANSPARENCY

Data Transparency

Exactly what this tool uses and how

Input
What it needs
Required:Existing heavy equipment (dump trucks, haul trucks, dozers, excavators), Vehicle specifications and dimensions, Site operational parameters
Optional:Existing GPS systems, Fleet management data, Custom safety requirements
Formats:Technical equipment specifications, Site maps and terrain data, Operational workflow documentation
Output
What you get
Format:Fully autonomous heavy equipment with real-time monitoring dashboard
Fields:Vehicle location and status, Operational commands and execution status, Safety alerts and compliance metrics, Productivity metrics (tons moved, cycles per hour), Equipment diagnostics
Algorithm
How it works
Model:SafeAI Autonomous Framework (SAF) - ASIL D-certified operating system purpose-built for heavy industry autonomous operations
Accuracy:ASIL D certification (highest automotive safety integrity level) achieved August 2023. Operational deployments with MACA (100+ vehicles) and Obayashi (300+ vehicles) demonstrate production-ready reliability
Privacy
How your data is protected
Retention:Not publicly specified; retained for duration of fleet operation and business relationship
Training:Not publicly specified
Compliance:ASIL D certification (ISO 26262), IEC 61508, ISO 13849, ISO 19014-4
API
Integration
Endpoint:Contact vendor for API access
Method:Not publicly documented
USE CASES

Use Cases

  • ·Autonomous haul truck fleets for large mining operations
  • ·Autonomous dump truck operations for construction sites and quarries
  • ·Autonomous dozer and grading operations for site development
  • ·Remote fleet management and monitoring across distributed work sites
  • ·Zero-emission autonomous vehicle deployment (via Siemens electrification partnership)
  • ·Safety-critical autonomous equipment operations in hazardous environments
PRICING

Pricing

Free
Not available
Pro
Retrofit cost and operational fee structure not publicly specified; custom quotes based on fleet size and equipment
Enterprise
Custom pricing and support agreements available
Sources & Research NotesResearch date: 2026-02-01
Data Sources (9)
Fields Checked (12)
problem, solution, deliveryMethod, disciplines, projectPhases, projectTypes, maturityStage, yearFounded, fundingTotal, customerBase, algorithm-asil-d-certification, compliance-certifications
Not Found (8)
integrations, pricing-structure, api-documentation, privacy-policy, data-retention-policy, data-training-practices, rate-limiting-details, detailed-technical-specifications
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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