Products
Analysis PlatformData Enhancement ServicesAnalysis PartnershipFree Super Sheet
Use Cases
AI Search IntelligenceExpanded Ad AnalyticsHistorical Document SearchEcomm & Product BIMore
PricingResourcesTeam
ENERGY & INFRASTRUCTURE

Mining & Resource Extraction Efficiency Analytics

Isolate the fuel and time waste hiding across your extraction operation.

Mining operations involve heavy capital expenditures, where slight inefficiencies in haul truck routing, ore processing, or equipment maintenance cost millions annually. Operations managers can upload haul truck GPS logs, ore grade assay reports, crushing equipment sensor streams, and fuel consumption records. The platform evaluates the complex intersections between haul road slope, payload weight, driver shift duration, and machinery wear to isolate fuel and time waste.

Mine managers can explore citation-backed findings to see which extraction pits yield the highest profit margin per ton extracted. By surfacing statistically significant operational efficiencies, mining enterprises can optimize equipment routing, lower diesel fuel consumption, extend heavy machinery lifespans, and boost net profit margins.

Expand
Rank Efficiency Drivers
Steep Haul Road
56%
High Payload
42%
Idle Time
32%
Old Tires
22%
Base Column
ops_factor ▾
Value Column
fuel_waste ▾
56%ofSteep Haul Road=Fuel Waste
predictive power
88%
waste count
168
haul count
300
see examples

Location Intelligence

Use Steeped AI's location intelligence to combine haul truck GPS coordinates with elevation models and pit slope data for deep spatial analysis. See which routes and grades quietly burn fuel and cycle time, so routing and pit sequencing decisions rest on real terrain, not a flat map.

Pit 3steep slope

Data Preparation

Use Steeped AI's data preparation to reconcile truck IDs, assay units, and sensor streams across mixed fleets before analysis. When every rig and lab exports its own format, Steeped AI standardizes them first, so relational metrics compare like with like.

Truck IDs "H-7", "haul7" and "7" were unified
Assay grades in 3 units were converted to one scale
Dropped 1,900 readings from a stuck fuel sensor
Removed column "legacy_ton" because 86% were blank
Duplicate cycle logs for one trip were merged
generating new dataset
NEW DATA

Statistical Significance Testing

Steeped AI's automated significance testing confirms which efficiency gains are real versus normal variation between shifts and pits. A university-grade, 3-part framework keeps every finding defensible, so a routing or fleet change is funded on evidence, not one good week of tonnage.

Efficiency Gain Confirmedp < 0.01 · FDR corrected

Insight Deeplinks

Every efficiency recommendation is backed by strictly accurate, non-hallucinated calculations from raw sensor data. A mine manager can trace a fuel-waste finding straight to the haul cycles behind it before rerouting a fleet or resequencing a pit, so multimillion-dollar calls rest on verifiable numbers.

Efficiency Finding Citations
1 56% of "steep haul" runs burned "excess fuel"
2 42% of "high payload" trips added "cycle time"
3 33% of "idle" logs traced to a "crusher queue"
4 27% of "tire wear" tied to "hard braking"
5 19% of "Pit 3" hauls beat the "margin target"
6 15% of "shifts" over 10h raised "error rate"
7 9% of "low grade" ore lifted "processing cost"

The insights are already in your data.

Ask your data anything. Get real findings ranked by impact, with AI reports your team can present and share on the spot.