Your always-on AI metallurgist

Kotva Kotva
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Always-on AI Metallurgist

SimonAI is a breakthrough in real time analytics for mineral processing. It's an intelligent setpoint advisor that uses the power of AI to support metallurgists.

Always-on AI Metallurgist

SimonAI provides actionable insights that enable end-to-end optimization across grinding/crushing, flotation & leaching processes.
This AI metallurgist analyzes your data around the clock and helps you get the most from your plant by identifying opportunities & recommending actions for improved recovery and throughput.


  • Data-driven recommendations: An always-on AI metallurgist spots opportunities and recommends actions to improve performance.
  • Aggregated activity log: A repository of changes made, along with comments, acts as an educational tool to improve performance.
  • Adherence analysis: Track recommendations and implementation in an aggregated view to see how the tool is utilized in the plant.
  • Impact tracking: Track SimonAI's continuous value in recovery & throughput: actual (current value), optimized (improvement available), and baseline (historical).

What It Does

  • Provides actionable recommendations
  • Learns from historical plant data to make proactive suggestions
  • Provides a single source of truth for production changes and increases production visibility
  • Tracks performance of both plant personnel and plant performance
  • Reduces the number of people involved in production adjustments, and empowers the operations team
  • Helps create site standards for production adjustments
  • Surfaces issues at the plant by leveraging the activity log and adherence data
How It Works

How It Works

  1. SimonAI combines advanced analytics models to recommend optimal setpoints, track which recommendations have been implemented, and visualize the impact of those changes via a standardized user interface that’s embedded into your existing operational workflows.
  2. The plant data goes through advanced cleaning & anomaly detection Our analytical models generate optimization recommendations
  3. The production team reviews the recommendations The operations teams executes the selected changes
  4. The Draslovka data science team reviews the performance
  5. System users review reports to view the impact of the changes over time

Excited to hear more about our analyzers? Contact us!

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