Product
The reliability layer for your AI vision systems
The reliability layer for your AI vision systems
After 2 years of research and development, our solution is now
functional and PoC-ready.
After 2 years of research and development, our solution is now functional and PoC-ready.
TrustalAI Vision qualifies, in real-time, the reliability of your 2D/3D vision models' predictions before they impact your system or operational decision.
Industrial vision systems perform well under nominal conditions. However, in real-world environments, several factors lead to instability, decreased performance, conservative decisions, or human over-control:
Terrain variability (lighting, material, sensor wear)
Progressive drifts
Out of distribution situations
Out of distribution situations
Unpredictable false positives / false negatives
Before TrustalAI
Entered data
Client AI Model
Prediction
Unreliable
downstream
decision
No notion of reliability
of the prediction
Before TrustalAI
Entered data
Client AI Model
Prediction
Unreliable
downstream
decision
No notion of reliability
of the prediction
After TrustalAI
Entered data
Client AI Model
Prediction
Context
data
(optional)
Reliability
metrics
Highly reliable
downstream
decision
Enhanced prediction with reliability thanks to TrustalAI
After TrustalAI
Entered data
Client AI Model
Prediction
Context
data
(optional)
Reliability
metrics
Highly reliable
downstream
decision
Enhanced prediction with reliability thanks to TrustalAI
AI predicts, TrustalAI Vision
tells you if it's reliable
The problem
AI optimizes at the cost of reliability
Today, AI optimizes overall performance, but very few AIs measure reliability with each detection or prediction.
Quantifying uncertainty is technically complex and research on this subject is very limited through the EU AI ACT
Companies focus on overall performance (accuracy, AP, etc.) rather than operational reliability
The existing tools (monitoring / drift) are based on open source like Evidently AI, which are useful for analysis but not designed for real-time, case-by-case prediction.
The problem
AI optimizes at the cost of reliability
Today, AI optimizes overall performance, but very few AIs measure reliability with each detection or prediction.
Quantifying uncertainty is technically complex and research on this subject is very limited through the EU AI ACT
Companies focus on overall performance (accuracy, AP, etc.) rather than operational reliability
The existing tools (monitoring / drift) are based on open source like Evidently AI, which are useful for analysis but not designed for real-time, case-by-case prediction.
Prediction
Opportunity
The missing element
TrustalAI adds the missing element: reliability through the analysis of each prediction, in real-time and Plug & Play.
Opportunity
The missing element
TrustalAI adds the missing element: reliability through the analysis of each prediction, in real-time and Plug & Play.
Why us?
Why us, why now?
We fulfill a strategic European need for AI reliability.
We are responding to a global demand: AI must be controllable.
We possess a rare combination: mastery of state-of-the-art AI and deep scientific expertise in metrology and uncertainty quantification.
Why us?
Why us, why now?
We fulfill a strategic European need for AI reliability.
We are responding to a global demand: AI must be controllable.
We possess a rare combination: mastery of state-of-the-art AI and deep scientific expertise in metrology and uncertainty quantification.
Compatible with your existing architecture
Works with all vision AIs (including black box)
Compatible with 2D and 3D vision
Available Embedded SDK / Edge / Cloud API
Full integration into the ML pipeline (training / validation / inference)
No change to your algorithmic heart
Works with all vision AIs (including black box)
Compatible with 2D and 3D vision
Available Embedded SDK / Edge / Cloud API
Full integration into the ML pipeline (training / validation / inference)
No change to your algorithmic heart