Trust Me, I’m An Engineer! [Building trust in AI/ML for applications in engineering]

When it comes to implementing data-driven modelling techniques (i.e. AI and machine learning), trust can be a major challenge and barrier to adoption.

While AI is recognised as an emerging technology, a point of difference in a solution, it also can be considered a buzz word for a black-box solution that is difficult to understand. To be able to trust and apply technology, engineering and operations teams need to be able to understand how it functions.

Trust takes times and can be built through consistent, technical proof that solutions function reliably in real world operations.

That’s why we use TRUST as a framework when delivering AI/ML solutions:

When engineers see evidence of these five qualities, they move from curiosity to confidence, and ultimately, adoption.

How we help

At SigML, we help organisations navigate the adoption of AI/ML by supporting with identifying use cases and implementing solutions. We offer the Nexgineer™ platform, providing a way for teams to run data-driven modelling alongside their analysis tools.

1. Validation against real-world behaviour: Every Nexgineer™ model is benchmarked against historical and real-time data so engineers can see exactly where it matches or deviates from actual outcomes. We maintain baseline ("golden") datasets for recurring back-tests and publish error ranges.
   

2. Side-by-side comparison: Predictions and recommendations are presented alongside the KPIs engineers already use (e.g., production rate, energy use, lap time, telemetry traces). Engineers can drill down to compare recommendations to prior events and actuals.
   

3. Full traceability & audit trail: Each recommendation is logged with input features, model version, training window, confidence intervals, and constraint set used. This supports audits, root-cause analysis, and continuous improvement.
   

4. Shadow mode first: In pilots, Nexgineer™ runs in shadow mode (advisory only), allowing teams to compare recommendations with actual decisions before enabling closed-loop actions.
   

5. Champion/Challenger: We can run multiple models in parallel; the challenger must outperform the champion on pre-agreed metrics before promotion.
   

When engineers are equipped with tools they can understand, validate, and control, advanced analysis and performance optimisation moves from being an interesting concept to part of daily decision-making. Which is how trusted solutions like Nexgineer turn technology into real-world value.

---

Curious how trust is built into every layer of Nexgineer™? Explore how it fits into your existing workflows and delivers value to your teams — contact us to request a walkthrough.