Mining operations generate terabytes of sensor, fleet, and process data every shift. Most of it is summarized into dashboards that no one acts on. Lumina connects geological, operational, and maintenance intelligence into a structured reasoning layer that surfaces decisions, not just data.
No sign-up required. Your data stays in your environment. Local-first architecture.
The Strategic Gap
The global mining industry generates 2.4 billion tonnes of waste annually, yet only 15% of mining companies have scaled AI beyond pilot (Deloitte, 2024). McKinsey estimates AI can improve mining productivity by 10 to 20%, but fragmented data systems and siloed expertise keep most operations stuck in reactive mode.
Block models are built during feasibility and rarely reconciled against production actuals. Grade control becomes a lagging indicator instead of a planning tool.
Average unplanned downtime for a haul truck costs $180,000 per hour. Most maintenance systems trigger alerts too late because vibration, oil analysis, and dispatch data live in separate silos.
Grinding and crushing consume up to 50% of a mine site's total energy. SAG mill optimization requires correlating feed hardness, charge levels, and throughput in real time, not in weekly reports.
Flotation circuits degrade gradually. By the time metallurgists identify a reagent dosing issue or froth stability change, tonnes of concentrate quality have already been lost.
Near-miss reports, fatigue monitoring alerts, and MSHA compliance data are collected but rarely correlated. Leading indicators exist in the data; they are just never surfaced together.
Mine planners use deterministic models with fixed commodity prices and static cutoff grades. When conditions change mid-quarter, re-planning takes weeks instead of hours.
What Lumina Does
Lumina does not replace your engineers or your existing systems. It adds a reasoning layer on top, turning the data you already collect into intelligence your teams can act on, verify, and trust.
Lumina's Exploration engine continuously runs hypothesis tests: anomaly clustering, correlation analysis, severity scoring. It delivers findings proactively, the way an experienced operator spots patterns that 'don't look right'.
Through the Dialogue Intelligence Framework™, a six-layer cognitive architecture, agents reason across all your data sources simultaneously. Fragmented data becomes connected understanding.
Configured by your engineers, trained on your procedures, compounding context over time. Each interaction builds a knowledge base that scales.
Tensions that take days in meetings are resolved in seconds. Agents vote, alert, and challenge each other, with every position grounded in data and rationale visible.
Every query is valid SQL. Every calculation executed by a database, not an LLM. When someone asks 'how did you calculate that?', the agent shows the query, the data, and the method.
Your data does not move to us. SaaS, private cloud, on-premises, or air-gapped. Bring your own LLM through Ollama, or export agents for Claude, Copilot, or any MCP-compatible tool.
How It Works
The Dialogue Intelligence Framework™ was designed for environments where decisions depend on correlating data from multiple engineering disciplines. In mining, that means connecting geological models, process plant telemetry, fleet management systems, and environmental monitoring into a single reasoning layer. Instead of building another dashboard, DIF enables structured conversations with your operational data, grounded in the physics and metallurgy your engineers already understand.
Ask questions in plain language. Agents respond with reasoned answers, suggest follow-up questions, and guide you toward the decision you need to make.
Proactive scan engine for your data: cluster analysis, correlation, Benford's law, severity scoring. Proactive hypothesis testing that surfaces what you did not know to look for.
Raw findings are synthesized into composites that connect cause, context, and consequence. Not just 'this is anomalous' but 'here is what it means for your operation'.
A six-engine pipeline (DSP, Policy, Semantic Guard, LLM, Classifier, Memory, Audit) judges what agents can access, say, and do. Per-agent configuration. Zero trust by default.
Agents build context over time through a three-tier memory architecture: Local knowledge, team-level awareness, and organizational intelligence that compounds with use.
Reasoning and tools are separated by design. LLMs plan the query. DuckDB executes the calculation on your data. No black-box results. Every step visible.
This is not a chatbot with a database connection. It is a complete cognitive architecture where every layer serves a purpose and every output is traceable.
Explore the Full DIF ArchitectureUse Cases by Domain
Open Pit, Underground, Drill & Blast
Extraction is where geological uncertainty meets operational execution. Every blast pattern, every grade control decision, and every geotechnical reading carries downstream consequences for recovery and cost. Yet most extraction teams rely on disconnected systems for drill logs, assay results, and slope monitoring. Connecting these data streams into a reasoning layer reduces dilution, improves fragmentation, and catches geotechnical risk before it becomes a safety incident.
Correlate blast pattern design with actual fragmentation outcomes. Analyze explosive energy distribution, burden and spacing ratios, vibration monitoring data, and downstream crusher throughput to close the loop between blast design intent and realized rock breakage.
Reconcile block model predictions against production assays in near real time. Track ore from face to stockpile to crusher, correlating blast-hole sampling with plant feed grade to reduce dilution and improve mine-to-mill reconciliation ratios.
Integrate slope stability radar, piezometer readings, prism surveys, and microseismic data into a unified risk picture. Surface early indicators of wall instability or subsidence before they escalate, and connect ground support design decisions to observed deformation trends.
McKinsey & Company, "How AI can unlock value in mining," 2023.
Comminution, Flotation, Tailings
The process plant is where ore becomes product, and where small deviations in feed characteristics cascade into major cost and recovery impacts. Comminution alone accounts for up to 3% of global electricity consumption. Flotation recovery is sensitive to dozens of interacting variables, from reagent chemistry to particle size distribution. Most plants monitor these in isolation. Connecting grinding, flotation, and tailings data into a single reasoning layer lets metallurgists act on the full picture rather than individual sensor readings.
Balance SAG mill throughput against energy consumption by correlating feed hardness (Wi), ball charge level, cyclone overflow P80, and bearing pressure. Identify optimal operating envelopes that maximize tonnes per kWh without sacrificing downstream recovery.
Track grade-recovery curves across flotation banks and correlate shifts with reagent dosing, froth depth, air flow, and feed mineralogy. Detect circuit instability early by reasoning across multiple cell-level measurements instead of relying on composite concentrate assays alone.
Monitor tailings storage facility (TSF) integrity by integrating piezometric levels, beach survey data, seepage flow rates, and geochemical stability indicators. Maintain water balance models that account for seasonal variability, process water recycling rates, and regulatory discharge limits.
Coalition for Eco-Efficient Comminution (CEEC), "Energy Curves," 2023.
Haulage, Maintenance, Mine Planning
Fleet operations represent 40 to 60% of open pit mining costs. A single ultra-class haul truck costs $5 to 7 million, burns over 300 litres of diesel per hour, and loses $180,000 per hour of unplanned downtime. Yet most fleet management systems report on lagging metrics: completed cycles, total fuel burned, reactive work orders. Connecting dispatch, condition monitoring, and planning data lets operations shift from scheduled maintenance to condition-based decisions, and from fixed plans to adaptive sequencing.
Analyze cycle times, queue times at loaders and dumps, payload distributions, fuel burn per tonne-kilometre, and tire wear patterns. Identify bottlenecks in the haulage network and quantify the cost impact of road conditions, dispatch logic, and shift-change delays.
Correlate vibration spectra, oil analysis trends, component temperature profiles, and historical failure modes to forecast remaining useful life. Move beyond calendar-based maintenance intervals by reasoning across multiple condition indicators for engines, final drives, and hydraulic systems.
Connect pit sequencing decisions to commodity price scenarios, geotechnical constraints, and processing capacity. Evaluate cutoff grade sensitivity, stockpile blending strategies, and phase sequencing trade-offs through structured reasoning instead of static spreadsheet models.
Caterpillar Inc., "Mining Fleet Management," 2023; Deloitte, "Tracking the Trends," 2024.
Across the Enterprise
Mining operations depend on more than technical disciplines. Workforce planning, financial modeling, safety compliance, and system reliability all shape site-level performance. Lumina provides structured reasoning across these corporate functions, connected to the same operational data your engineers use.
Correlate crew composition, competency records, and fatigue monitoring data with productivity outcomes. Identify how roster design, training gaps, and experience levels affect shift-level performance across extraction and processing.
Track cost per tonne mined, cost per tonne milled, and royalty obligations against production actuals. Connect financial models to operational drivers so variance analysis explains why costs moved, not just that they did.
Integrate fatigue management system alerts, near-miss reports, hazard observations, and MSHA or provincial regulatory requirements into a unified compliance picture. Surface correlations between operational conditions and incident patterns.
Monitor the reliability of dispatch systems, process historians, fleet management platforms, and communication networks across remote site infrastructure. Correlate system outages with operational impact to prioritize IT investment decisions.
Deployment
Five deployment levels from public cloud to fully air-gapped. Bring your own LLM or use ours. Export agents to operate inside Claude, Copilot, or any MCP-compatible tool.
From geological models to plant telemetry to fleet dispatch, Lumina builds a reasoning layer across your entire operation. See how structured AI applies to your site.
Learn how DIF structures human-AI reasoning for complex industrial environments where decisions span multiple data sources and engineering disciplines.
Deep DiveWhy local-first, on-premise AI architecture matters for mining operations in remote locations with limited connectivity and strict data sovereignty requirements.
InsightMining has invested heavily in visualization. The problem was never visibility. It was turning visible data into structured decisions at the speed operations demand.
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