AI-based Data Fusion
Unify information
Reveal new insights
Turn data richness into value
Decide with clarity
Why fuse data?
Each data type brings a different piece of information; together, they provide a more complete view. This is precisely the goal of multimodal fusion: to achieve more refined analyses, more robust in challenging conditions.
The result: greater accuracy and better support for decision-making.
Examples of common modalities:
- Acoustic imaging (ultrasound, vibroacoustics, etc.)
- Hyperspectral imaging (Infrared, X-ray, RGB)
- Mapping data (topography, hydrology, cadastral, etc.)
These are typically the kinds of data I need to leverage
Keeping only the useful info,
at the right time (gating)
What is gating?
It is a mechanism specific to certain neural networks that controls the flow of information within the model.
“Gates” learn to open or close access to certain data streams, keeping only the information relevant to the analysis, at every moment.
What is it used for?
- On time-based data (video, sensor series), it keeps the useful context and reduces noise.
- In fusion, the combination becomes adaptive: each data type contributes at the right time depending on its relevance.
- Result: more stable outputs and more consistent results.
Typical use cases in multimodal fusion
Covered domains
- Quality control & predictive maintenance
- Non-destructive testing
- Mobility / Automotive
- Diagnostic support
Key benefits
- Robustness against variations (lighting, noise, occlusions)
- Reduced ambiguity thanks to data complementarity
- A common foundation that eases adaptation to new contexts
Our fusion architecture
We have designed a general-purpose neural network architecture for rasterized datasets (2D grid data) and associated signals. It quickly adapts to new contexts without heavy R&D investment.
In practice, it integrates:
- By data type: a tailored process for each format (image, sensor, text, map).
- Multi-stage fusion: combining information at different stages for better cross-analysis.
- One foundation, multiple uses: classification, detection, measurement… without starting from scratch.
Same problem, new data?
We reconnect encoders, we lightly retrain — the base itself remains stable.