Product
The reliability layer
for your AI predictive models
The reliability layer
for your AI predictive models
Stemming from our R&D in
uncertainty quantification, Predictive adds a confidence interval to every forecast. Validated in a real operational environment (TRL9).
Stemming from our R&D in uncertainty quantification, Predictive adds a confidence interval to every forecast. Validated in a real operational environment (TRL9).
Stemming from our R&D in uncertainty quantification, Predictive adds a confidence interval to every forecast. Validated in a real operational environment (TRL9).
TrustalAI Predictive qualifies, in real-time, the reliability of each forecast from your predictive models, before it impacts your system or your operational decision.
Predictive models perform well on data they have already seen. But in a real-world environment, several factors degrade their forecasts and weaken downstream decisions:
Variability of real-world conditions
Concept drift
Situations outside the scope of validity
Situations outside the scope of validity
Alerts on undetected failures
Alerts on undetected failures
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 Predictive
tells you if it's reliable
The problem
A prediction without a confidence level remains a gamble
A predictive model delivers a number, never the confidence that can be placed in it. High-stakes decisions are then made without knowing if the current forecast deserves to be followed.
A good average accuracy (RMSE, MAPE) hides the forecasts where the model is heavily mistaken, precisely those that should not be followed.
Faced with an unprecedented process or gradual drift (model drift), the model continues to predict with the same confidence, without signaling that it is going outside its domain of validity. Alerts arrive without a confidence level, and the drift sets in.
Monitoring tools detect drift after the fact, based on aggregated history. By the time the alert is triggered, the decision is already made and the incident has already incurred a cost.
The EU AI Act now requires demonstrating the reliability of every decision made by high-risk systems.
The problem
A prediction without a confidence level remains a gamble
A predictive model delivers a number, never the confidence that can be placed in it. High-stakes decisions are then made without knowing if the current forecast deserves to be followed.
A good average accuracy (RMSE, MAPE) hides the forecasts where the model is heavily mistaken, precisely those that should not be followed.
Faced with an unprecedented process or gradual drift (model drift), the model continues to predict with the same confidence, without signaling that it is going outside its domain of validity. Alerts arrive without a confidence level, and the drift sets in.
Monitoring tools detect drift after the fact, based on aggregated history. By the time the alert is triggered, the decision is already made and the incident has already incurred a cost.
The EU AI Act now requires demonstrating the reliability of every decision made by high-risk systems.
Prediction
Opportunity
The missing element
TrustalAI adds the missing element: reliability through the analysis of each prediction — a plug & play, real-time confidence interval.
Opportunity
The missing element
TrustalAI adds the missing element: reliability through the analysis of each prediction — a plug & play, real-time confidence interval.
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 combine an advanced mastery of predictive AI with 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 combine an advanced mastery of predictive AI with deep scientific expertise in metrology and uncertainty quantification.
Compatible with your existing architecture
Works with all predictive models (including black box)
Compatible with time series & scoring
Model drift detection
Available Embedded SDK / Edge / Cloud API
Full integration into the ML pipeline (training / validation / inference)
Real-time: edge <20 ms / cloud <80 ms (<100 ms)
No change to your algorithmic heart
No change to your algorithmic heart
Works with all predictive models (including black box)
Compatible with time series & scoring
Model drift detection
Available Embedded SDK / Edge / Cloud API
Full integration into the ML pipeline (training / validation / inference)
Real-time: edge <20 ms / cloud <80 ms (<100 ms)