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Sensmore

Retrofit radar-and-AI kit that automates existing mining and quarry machinery

Sensmore is a Berlin/Potsdam-based robotics startup that retrofits existing, vehicle-agnostic heavy mobile machinery (haul trucks, wheel loaders, dumpers, and underground LHDs) with 4D radar, cameras, and a proprietary two-layer "Physical AI" stack. The resulting platform (Site OS, Machine Assist, Eye, Live Mapping, Loader Automation) delivers collision warnings, autonomous load-haul-dump cycles, and site-wide operational visibility for mining, quarry, and raw-materials sites.

$7.3M total disclosed (~€6.5M-€6.4M Series A per differing press accounts, announced May 2025), led by Point Nine Capital with Acequia Capital, Tiny Supercomputer Investment Company, Prototype Capital, Entrepreneur First, several angel investors, the State of Brandenburg, and EU regional development funding raisedEarly Stage
Founded 2022 · ~15 employees (per Tracxn, as of April 2026). Named customers confirmed via press/case studies: CEMEX (3-year relationship; full digital/automated quarry rollout at Rüdersdorf, Germany), Lhoist (Flandersbach quarry), and Heidelberg Materials (named on vendor homepage, no case study detail found). Silver medal winner, "Future of Construction" vertical, Cemex Ventures Construction Startup Competition 2023.
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WHY IT EXISTS

Why This Tool Exists

The Problem

Mining and quarry operators run large fleets of haul trucks, wheel loaders, and underground LHDs that depend on manual operation, exposing workers to collision and underground-hazard risk and capping machine utilization at the hours a human can safely work per shift (vendor cites ~5 hours in demanding underground conditions). Purely camera-based automation systems also degrade in the rain, fog, dust, and low-visibility conditions common on these sites.

The Solution

Sensmore retrofits existing heavy machinery with 4D radar and camera sensors plus a two-layer AI stack — a fast reactive control network ("Thinking Fast") for real-time obstacle avoidance and a Vision-Language-Action Model reasoning layer ("Thinking Slow") for task planning — to deliver collision warnings (Machine Assist), autonomous load-haul-dump cycles (Loader Automation), computer-vision site monitoring (Eye), and site-wide visibility (Site OS). Radar-based sensing is designed to keep functioning in weather and visibility conditions that degrade vision-only systems.

USAGE

How You Use It

Delivery Method
Hardware (retrofit kit: 4D radar, cameras, onboard compute)SaaS (Site OS dashboard)
Integrations
Not publicly specified — positioned as a vehicle-agnostic retrofit kit rather than a software integration; partners with equipment manufacturers (e.g., Aramine, for the Aramac L140B autonomous underground LHD) at the hardware/OEM level rather than via disclosed software integrations
Disciplines
Construction, Civil Engineering
Workflows
Coordination & Delivery
Project Phases
Construction Administration
Project Types
Infrastructure, Industrial
TRANSPARENCY

Data Transparency

Exactly what this tool uses and how

Input
What it needs
Required:Existing heavy mobile machinery to retrofit (haul trucks, wheel loaders, dumpers, underground LHDs), Site layout/mapping data
Optional:CSV/DXF site blueprints, Fleet telemetry
Formats:4D radar sensor data, Camera video feed, GNSS/positioning data
Output
What you get
Format:Site OS web dashboard plus in-cab Machine Assist alerts and autonomous machine control
Fields:Real-time collision warnings, Live site and machine positioning maps, Autonomous load-haul-dump cycle execution, Productivity/utilization data, Computer-vision-based site monitoring flags (Eye)
Algorithm
How it works
Model:Proprietary two-layer "Physical AI" stack: a fast reactive end-to-end network ("Thinking Fast") plus a Vision-Language-Action Model reasoning layer ("Thinking Slow"); specific model architecture and parameter counts not publicly disclosed
Accuracy:Not publicly specified — no independent benchmarks published. Vendor-reported field result: an automated Aramac L140B underground loader operates autonomously up to 8 hours per shift vs. ~5 hours in manned mode at CEMEX's Rüdersdorf site; a separate customer testimonial cites up to 30% downtime reduction (methodology not published).
Privacy
How your data is protected
Retention:Not publicly specified for operational/machine sensor data collected on customer sites. The general privacy policy (covering website visitors and job applicants) states personal data is "routinely blocked or erased in accordance with legal requirements," with job-applicant data erased two months after a refusal decision.
Training:Not publicly specified whether customer site/sensor data (radar, camera, machine telemetry) is used to train Sensmore's AI models.
Compliance:GDPR-compliant privacy policy (confirmed on official site, v1.1, last edited September 2024), No SOC 2, ISO 27001, or other third-party security certifications publicly disclosed
API
Integration
Endpoint:Not publicly documented — no developer/API portal found
Method:Not publicly specified
USE CASES

Use Cases

  • ·Retrofitting existing haul trucks and wheel loaders with collision-warning and precision-positioning assistance (Machine Assist) at mining and quarry sites
  • ·Autonomous load-haul-dump (LHD) cycles for underground mining equipment, reducing operator exposure to underground risk
  • ·Computer-vision-based site monitoring and quality checks (Eye)
  • ·Real-time, site-wide operational visibility and material-flow coordination (Site OS)
  • ·Extending a machine's autonomous operating hours per shift beyond what is achievable with a human operator (vendor cites ~8 hours autonomous vs. ~5 hours manned for one underground loader)
PRICING

Pricing

Free
No free tier; enterprise sales model
Pro
Not publicly specified
Enterprise
Custom pricing based on fleet size and site scope; contact via vendor website scheduling link
Sources & Research NotesResearch date: 2026-07-17
Data Sources (14)
Fields Checked (11)
problem, solution, deliveryMethod, disciplines, yearFounded, fundingTotal, customerBase-named-accounts, useCases, privacy-gdpr-compliance, algorithm-approach-general, cemex-construction-startup-competition-2023-result
Not Found (9)
algorithm-model-architecture, accuracy-benchmarks, public-api-documentation, api-authentication-and-rate-limits, exact-data-hosting-location, ai-training-data-use, pricing-figures, named-software-integrations, total-employee-count-beyond-third-party-estimate
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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