FxInformed

Detect the Unexpected. Optimize your performance.

AI-powered anomaly detection for manufacturing & engineering data.

Your production and R&D data already contain early signals of future failures.

FXInformed turns them into operational decisions before they impact cost, quality, or delivery.

Signal Surface

StationsVariance
Live

FXInformed is not a dashboard.

It is a manufacturing intelligence layer.

Manufacturing data is underused.

Threshold logic was designed for control. Not for prediction.

If you only look at thresholds, you miss the real signal.

Most companies rely on PASS/FAIL thresholds.
Defects are rarely caused by a single parameter.
Interactions between variables are invisible.
Drifts happen gradually.
False positives are expensive.
Internal AI teams are costly and slow to build.

We detect anomalies before they become failures.

We model multivariate interactions that traditional dashboards and rule-based systems cannot detect.

01

We analyze your existing data.

02

We identify hidden patterns and parameter interactions.

03

We build reusable ML models.

04

We deploy them via secure SaaS platform or API integration.

Results
Fewer unexpected failures
Faster root-cause analysis
Reduced testing waste
Clear operational KPIs

A De-Risked Engagement Model

Low commitment

Initial Data Assessment

We evaluate data usability and potential value.

You see value quickly

Insight & Model Phase

Custom anomaly detection model + actionable report.

Deployment

Continuous monitoring via SaaS platform or API.

Automated anomaly detection on new data

Who it’s for

For executives

  • Reduce production & testing costs
  • Early risk detection
  • Clear KPI visibility
  • No need to build internal AI team

For engineers

  • Ready-to-use ML models
  • Discover hidden R&D insights
  • Identify issues before final design

For operations & supply chain

  • Detect process drifts
  • Monitor supplier variability
  • Predict maintenance signals
  • Optimize testing strategy

Real questions we Answer

Stop guessing. Start measuring interactions.

Which parameter contributes most to failures?
Are defects increasing over time?
Is supplier change improving PASS/FAIL performance?
Is degradation signaling upcoming maintenance?
Is there an abnormal demand spikes identification ?
Do you observe an unusual inventory or lead time deviations ?

Why companies work with us

Fast time-to-insight
No heavy internal setup
Built for hardware data
End-to-end support
Secure data handling
Built for Industrial Data
Designed for high-dimensional hardware test data
Scalable to millions of test records
Model monitoring and retraining included
Enterprise-grade data security

Your data already contains tomorrow’s failures.

The question is whether you detect them in time.