Canadian industrial-sensing company providing sensor and data systems for material characterization and operational decision support.
Operating context
MineSense characterizes ore at the extraction face rather than imaging deposits remotely. ShovelSense places high-speed X-ray fluorescence sensors on mining machinery and processes bucket measurements using orebody-specific models. Grade and material classifications flow into fleet-management systems to route loads before ore is sent to waste or waste to the mill. The Vancouver company reported 32 systems at 16 mines in February 2026 and appointed John Slaven CEO effective March 2. These company-reported deployment figures establish commercial scale while leaving site economics and recovery improvements dependent on local geology, calibration and operational integration. Mine operators should compare sampled-grade validation, routing rules, uptime and reconciliation against mill results before extrapolating advertised gains.
Canadian footprint
MineSense has a documented Canadian operating presence in Vancouver, British Columbia, Canada. Its public organization and capability evidence should be read at that location and national scope only where the cited sources explicitly support it.
Vancouver, British Columbia is the published city-level context for MineSense. It does not imply a street address or exact facility location.
ShovelSense uses machinery-mounted X-ray fluorescence sensors and orebody-specific analytics to classify bucket material during digging and send grade information to fleet-management systems for routing.
The publicly described Industrial sensing and data analytics offering may be relevant to monitoring or remote-operation use cases; this is a derived mapping for reviewer assessment.
Released Defence need · Reviewed connectionEvidence strength: Moderate
Industrial sensing and data analytics may help teams working on Sovereign defence capability areas by contributing to detection and situational awareness, cyber and digital operations, and advanced materials and manufacturing.
Reviewed connections indicate possible relevance based on public evidence. They do not indicate procurement direction, eligibility, endorsement or customer interest.
Questions for a first conversation
01
How will the orebody model be calibrated and validated against assays, and how are routing decisions reconciled with mine-to-mill results?
Adds the XRF-to-routing mechanism and bounded commercial deployment scale.
Original sources 4 linked records
These records support the organization, its owned capabilities and their reviewed connections. A source count is not a count of independent confirmations.
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: Profile · operating context
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · summary
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · commercial availability
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · technical tags
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · maturity
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · capability type
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · core features
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · core features
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · name
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · technical tags
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · technology readiness level
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · technical tags
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · core features
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · defence applications
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · technicaldomainslugs
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · defence applications
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · technicaldomainslugs
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
Supports: ShovelSense ore characterization and routing · core features
MineSense ShovelSense mounts high-speed X-ray fluorescence sensors on mining machinery, measuring bucket material during the dig cycle. Orebody-specific machine-learning processing produces grade/material classification and passes routing information to fleet-management software. It measures material composition, not underground imaging or autonomous vehicle control.
MineSense announced John Slaven as CEO effective March 2, 2026, and reported 32 systems operating at 16 mines globally. It states ShovelSense identified over 19,000 tons of copper otherwise headed to waste as of 2025. These are company-reported scale/impact figures, not independent recovery measurements. Vancouver contact address is 8508 Ash Street.
Supports: Profile · operating context
MineSense announced John Slaven as CEO effective March 2, 2026, and reported 32 systems operating at 16 mines globally. It states ShovelSense identified over 19,000 tons of copper otherwise headed to waste as of 2025. These are company-reported scale/impact figures, not independent recovery measurements. Vancouver contact address is 8508 Ash Street.
MineSense's public evidence establishes its current role and offering, but it does not answer every access, configuration, eligibility, maturity or buyer question needed for a first business-development conversation.
Supports: Profile · operating context
The official capability material supports the named Industrial sensing and data analytics offering and the bounded operational description prepared for reviewer consideration.
Supports: Profile · description
The official company material describes MineSense as a Canadian technology business with the public offering summarized in this candidate.
Supports: ShovelSense ore characterization and routing → Sovereign defence capability areas
Canada needs secure strategic autonomy and reliable access to capabilities that are critical to defending its sovereignty and meeting allied commitments, while reducing exposure to foreign supply-chain and intellectual-property dependencies.
Supports: ShovelSense ore characterization and routing → Sovereign defence capability areas
Reviewer synthesis of the public end state: Canada can build, sustain or control priority aerospace, ammunition, digital, in-service support, sensor, space, specialized manufacturing, training and uncrewed capabilities through domestic strength, trusted partnerships and sovereign access.
Public sources cited · Facts and assessments kept separate · Human review
MineSense announced John Slaven as CEO effective March 2, 2026, and reported 32 systems operating at 16 mines globally. It states ShovelSense identified over 19,000 tons of copper otherwise headed to waste as of 2025. These are company-reported scale/impact figures, not independent recovery measurements. Vancouver contact address is 8508 Ash Street.
MineSense announced John Slaven as CEO effective March 2, 2026, and reported 32 systems operating at 16 mines globally. It states ShovelSense identified over 19,000 tons of copper otherwise headed to waste as of 2025. These are company-reported scale/impact figures, not independent recovery measurements. Vancouver contact address is 8508 Ash Street.
MineSense announced John Slaven as CEO effective March 2, 2026, and reported 32 systems operating at 16 mines globally. It states ShovelSense identified over 19,000 tons of copper otherwise headed to waste as of 2025. These are company-reported scale/impact figures, not independent recovery measurements. Vancouver contact address is 8508 Ash Street.
True North Map derives this first-conversation question from the documented MineSense capability, current evidence and the decision gap that remains.
Supports: Profile · canadian footprint
The official capability material supports the named Industrial sensing and data analytics offering and the bounded operational description prepared for reviewer consideration.
Supports: Profile · editorial profile version
MineSense has sufficient mapped public evidence for private reviewer consideration of the shared editorial profile template.
Supports: ShovelSense ore characterization and routing · summary
The official capability material supports the named Industrial sensing and data analytics offering and the bounded operational description prepared for reviewer consideration.
Supports: ShovelSense ore characterization and routing → Sovereign defence capability areas
The official capability material supports the named Industrial sensing and data analytics offering and the bounded operational description prepared for reviewer consideration.
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