6 Ways AI Can Boost Productivity in Your Engineering Department

Artificial Intelligence in Engineering isn’t just a trend. 

t’s a tool that, when applied correctly, can significantly accelerate the daily work of engineering teams.

Today we introduce Neureco — a practical AI tool designed for those working with physical systems, simulations, control strategies, and data from test benches or field operations.

Its purpose? To generate digital twins that can be applied across different areas of the engineering department.

No coding skills required.
No black-box models you can’t understand.
Just a simple interface and lightweight, interpretable, integrable models.

Here are 6 real-world scenarios where using Neureco AI can truly make a difference.

1. For simulation engineers: explore more scenarios in less time

When working with complex physical models, each simulation can take hours, days, or even weeks—especially with 3D CFD models for external aerodynamics. This makes it difficult to explore design variations or alternative operating conditions beyond the scope of the original model.

With Neureco, you can build a surrogate model—a lightweight and fast version of the system based on real data, yet fully interpretable. A few well-chosen runs are enough to train the model. The result?

A data-driven, interpretable surrogate model that:

• interfaces with simulation and control environments,

• runs in real time,

• enables rapid design space exploration, even with many parameters.

It’s ideal for speeding up sensitivity analysis, optimization, or validation—without compromising on physical consistency or waiting all weekend for a batch to complete.

For test engineers: move some testing to the virtual world

If you’ve already collected data from functional or environmental tests, Neureco helps you turn that data into a predictive, data-driven model that:

• reproduces system behavior under known conditions;

• estimates unmeasured variables if they correlate with available signals;

• allows fast “what-if” scenario exploration—no need to rerun tests physically.

In short: a virtual test bench that’s always available—fast, flexible, and nearly cost-free to operate.

For field monitoring: detect anomalies reliably

You don’t need millions of data points to get started. A well-sampled history of key physical variables (like pressure, temperature, current, speed, or vibration) is enough to train a Neureco model that can:

• learn the system’s nominal behavior under real operating conditions;

• detect statistically abnormal deviations in real time, even without labeled failures;

• integrate with edge or cloud platforms for local or centralized analysis.

Neureco doesn’t rely on supervised classifiers (which require labeled failure data). Instead, it uses nonparametric neural models that learn normal dynamic patterns and flag deviations.

This enables robust predictive monitoring—even in the absence of a full failure-mode database. It’s especially valuable for complex or poorly documented systems where “normal” behavior is easier to define than every possible fault.

For maintenance planning: estimate wear and failure risk from real signals

Predictive maintenance doesn’t require complex physics-based models or massive labeled datasets. Often, a consistent history of sensor signals (pressure, torque, vibration, etc.) is enough to detect gradual behavioral shifts.

Neureco can:

• learn functional correlations between signals under healthy conditions;

• build a dynamic model of correct system behavior;

• identify growing or systematic prediction errors that signal wear or degradation.

This makes it possible to:

• detect issues before they become failures,

• flag slow drifts (like decreasing pressure or actuator delays),

• set adaptive alarm thresholds without explicitly classifying all failure modes,

• compare similar assets and identify those diverging from “normal” (fleet analytics).

Unlike static thresholds or simple trend lines, Neureco uses a compact, interpretable neural model that adapts to changing conditions. If simulations are available, it can even be trained or initialized with virtual data, making it useful in early development phases too.