We anticipate how the plant will respond before the ore is processed and recommend concrete operational decisions in real time.
You don't need a large project to get started. We validate impact using your own data in a matter of weeks.
How we validate impact in your operation
Instead of long, uncertain projects, we work with a rapid validation approach:
we use historical data from your operation to build models that replicate the actual behavior of the plant.
This allows us to simulate past operational decisions and measure how much recovery,
stability, or energy consumption could have been improved.
Result: a concrete business case before any implementation.
A structured, low-risk way to demonstrate real impact using your own data.
Designed to work with existing infrastructure, without disrupting operations.
The system uses data your operation already generates: plant historians, SCADA/DCS systems, laboratory, dispatch, and maintenance. No new instrumentation required to get started.
It initially operates as an analysis and recommendation layer. It does not intervene directly in plant control until results have been validated.
System outputs are concrete actions: blend adjustments, setpoint changes, or suggested interventions. The operator always remains in control.
Once impact is validated, the system can be integrated more deeply — automating decisions or connecting directly with operational systems.
Simplified architecture
Operational data (SCADA / historians / laboratory) →
behavior models →
decision engine →
real-time operational recommendations
The system adapts to your operation's existing architecture, whether on-premise or cloud-based.
Minimum requirements to begin a pilot validation.
Access to operational data from recent months (process, laboratory, or maintenance).
Someone on your team who understands the process and can validate operational decisions.
Define one priority: recovery, stability, energy, or maintenance.
Typical results observed in the industry through models applied to operational data.
Estimated increase of 1% to 3% through anticipatory adjustment of process variables.
Potential reduction of 5% to 10% by optimizing the plant's operating point.
Fewer instability events through early detection of process deviations.
We use the data your operation already generates — from the mine to the plant — to anticipate future behaviors and deliver concrete recommendations.
Recovery is defined before ore enters the plant, but today decisions are made without full visibility of their impact. Our system anticipates expected recovery based on incoming ore and recommends concrete operational adjustments in blending, grinding, and reagent dosing.
Most operational losses don't come from critical failures — they come from small accumulated deviations that go unnoticed. The system continuously monitors process variables and detects conditions that precede instability, enabling intervention before they impact production.
The plant operates most efficiently within specific ore ranges, but blending is typically defined by static rules or availability. The system evaluates the impact of each material combination before processing and recommends the feed sequence that maximizes stability and recovery.
Running at maximum throughput does not always maximize economic results. The system continuously calculates the optimal operating point considering energy, recovery, and ore type, recommending load and feed adjustments accordingly.
Failures are preceded by subtle changes in equipment behavior that are not visible through traditional monitoring. The system detects these early signals and estimates failure probability, recommending when to intervene to avoid unplanned stoppages.
Extraction and processing decisions are usually optimized separately, creating systemic inefficiencies. The system connects both stages, anticipating how the ore extracted today will impact the plant and recommending coordinated adjustments across the operation.
In a few weeks we can show you, using your own data, how much additional value your operation could generate through anticipatory decisions.
Request Pilot Evaluation →