Most decision-support tools stop at a single mine, a single subsystem, or a deterministic rollup. ChainVision is the integrated probabilistic layer across the full mining portfolio, from extraction through to product sale.
Built for operators, developers, and capital providers who need risk-quantified answers, not point-estimate plans. Iron ore to lithium, established commodities and the new critical minerals.
It tells you what should happen if everything goes to plan. It doesn't tell you the probability that it will.
Haulage simulation. Process plant control. Orebody calibration. None quantifies risk across the full chain at the planning horizon.
Capital allocation across operations is treated as scenario-by-scenario, not as a probability distribution under correlated drivers.
Aggregate across independent operational assets under shared stochastic drivers (commodity prices, FX, inflation). Track probability of meeting corporate guidance against board-approved business plans, with uncertainty propagated through every node and across every asset, not collapsed to a deterministic rollup.
Node library spans iron ore and copper through to lithium hard rock and brine, REE separation, HPAL nickel/cobalt, vanadium, antimony, tin, and battery-grade graphite. Built for the chemistry chains that gold-and-copper-heritage tools were not designed for, without abandoning the established commodities.
Methodology benchmarked against publicly reported quarterly production: ±1% on Pilbara Pilgangoora, ±3% on Lynas Mt Weld, using only data the operators have made public. Direct operator engagement tightens the model further; the public-data figures represent the floor we can demonstrate today.
Run scenarios, compare side-by-side, share with stakeholders, all in-platform. No multi-month implementation. The methodology engine remains on Copula Labs infrastructure; private cloud deployment is available for operations where data residency requires it.
Heads of asset strategy, planning, finance at producing miners.
"What's the probability we meet next year's guidance under shared commodity uncertainty across our portfolio?"
Probabilistic simulation across independent assets under correlated price and FX drivers, with chain-model parameters fitted to your historical production. P10/P50/P90 envelopes for compliance-to-plan reporting at board level.
Project directors, development VPs, CFOs at projects approaching FID.
"How do we present probabilistic NPV envelopes to ECA credit committees and FID approval?"
Probabilistic NPV envelopes with explicit Monte Carlo over commodity prices, recoveries, capex, and FX. Defendable risk quantification for export credit agency debt sizing and board investment decisions.
Project directors, heads of technical services at post-DFS projects.
"What's the probability of meeting our offtake commitments over the contract term?"
DFS uncertainty stress-testing with explicit shortfall-risk quantification. Discrete event simulation of planned and unplanned outages, mapped to offtake contract penalty exposure.
Export credit agencies, project lenders, equity capital providers.
"Can we trust the operator's NPV? What's the actual risk envelope?"
Independent third-party probabilistic envelope, benchmarked against publicly reported operator data. Risk-quantified deliverables for credit committees that go beyond deterministic financial models.
Graph-based interface with stream-type validation. Drag and drop unit operations. Connections that are not physically valid are rejected.
Orebody grade, recovery, equipment availability, commodity prices, FX. Use built-in distributions or supply your own.
Monte Carlo simulation with copula correlations. Optional optimisation layer. Outputs include P10/P50/P90 envelopes and full distribution shape per metric.
History-match chain-model parameters against your production data, or against published data where direct access is restricted. The forecast envelope tightens with each fitting cycle.
Two real Australian operations, two published quarterly production series, two ChainVision runs. The model reproduces the published actuals within ±1% on Pilgangoora and ±3% on Mt Weld, using only data the operators have made public.
Calibration error: ±1% on quarterly production tonnage
Calibration error: ±3% on quarterly production tonnage
The validation runs use only data that the operator has published in quarterly reports and investor presentations. ChainVision's chain-model parameters were tuned against the first portion of the published series, then the model was run forward to predict the held-out remainder. The error reported is the absolute deviation between simulated and reported quarterly production. The figures here represent what we can demonstrate against public data alone; the methodology tightens further when an operator engages directly and provides access to internal data.
The world needs more minerals than the existing supply base can deliver. Easy ore bodies are gone. Grades are falling. Plants are running variable feedstock through circuits that were not designed for the chemistry. Capital allocators are pricing risk that operators struggle to quantify.
The gap between operators with internal digital capability and those without is widening. The largest miners have spent a decade and hundreds of engineers building integrated decision-support systems. Mid-tier operators and developers do not have that scale or that timeline.
ChainVision is the integrated decision-support layer those mid-tier portfolios need, delivered as a product rather than a multi-year build.
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