Smart partner for the energy sector.

Visualize. Understand.
Calculate. Plan.
Machine learning, intelligent algorithms, and AI to optimize and plan the grid, manage assets, and improve asset quality.

Why Qwi

A team of engineers with deep industry expertise. We transform traditional operations through tailor-made innovative solutions that provide deeper insight, improving efficiency and reliability to enable more accurate planning and drive operational and economic impact.
+300K
kilometers of grid analyzed
+8K
corrected measurements
+25
years of experience

Common challenges

Incorrect measurements and inconsistent data.
Non-observable areas of the grid.
Difficulty identifying efficient switching operations in contingency simulations.
Smart grids and flexibility: growing operational complexity.
Inaccurate capacity maps and non-guaranteed power availability that slow down decision-making.
Gaps in the physical inventory that compromise the accuracy of the electrical digital twin.

What is really happening in my grid?

KUMU reconstructs a complete and consistent model of the grid, even when:
  • Measurements are missing
  • Data contains errors
It provides a real-time view of the actual grid status, creating a solid foundation for both operations and planning.

How can you identify the optimal switching operations in contingency simulations?

OLAI simulates every viable alternative and provides:
  • Optimal restoration switching operations
  • Accurate load flows for an overload- and undervoltage-free grid
  • Simulation of new assets for precise capacity maps

Do you have an accurate view of your assets and their condition?

MAILUNA performs advanced asset detection using AI and LiDAR processing, delivering:
  • Pole type identification
  • Ground wire count
  • Ground clearance and vegetation distance analysis
  • Tilt and avifauna detection

Do you have real visibility into non-technical losses?

In low-voltage networks, fraud and non-technical losses are difficult to detect.

HUNA uses the electrical model and historical data to identify abnormal consumption patterns, helping to:
  • Detect potential fraud
  • Prioritize inspections
  • Recover lost revenue

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