Our technologies learn from big, complex and various data
to solve real-world problems at the desired level of autonomy and automatism
to solve real-world problems at the desired level of autonomy and automatism
Using multi-source data
We give value to real-world data and images stored and updated in archives, docs, databases, machine log data, and collected from sensors and devices in the visible and invisible spectrum.
We give value to real-world data and images stored and updated in archives, docs, databases, machine log data, and collected from sensors and devices in the visible and invisible spectrum.
Using multi-source data
We give value to real-world data and images stored and updated in archives, docs, databases, machine log data, and collected from sensors and devices in the visible and invisible spectrum.
We give value to real-world data and images stored and updated in archives, docs, databases, machine log data, and collected from sensors and devices in the visible and invisible spectrum.
Involving users
According to the required level of interaction, we include users in the data-analysis process, and provide participatory, user-driven decision support systems.
According to the required level of interaction, we include users in the data-analysis process, and provide participatory, user-driven decision support systems.
Involving users
According to the required level of interaction, we include users in the data-analysis process, and provide participatory, user-driven decision support systems.
According to the required level of interaction, we include users in the data-analysis process, and provide participatory, user-driven decision support systems.
Organize and select data
Profile, normalize, filter, avoid redundancy, structure, standardize and integrate multi-source data to provide correct, unique and consistent data for analysis.
Profile, normalize, filter, avoid redundancy, structure, standardize and integrate multi-source data to provide correct, unique and consistent data for analysis.
Organize and select data
Profile, normalize, filter, avoid redundancy, structure, standardize and integrate multi-source data to provide correct, unique and consistent data for analysis.
Profile, normalize, filter, avoid redundancy, structure, standardize and integrate multi-source data to provide correct, unique and consistent data for analysis.
Applying advanced data analytics
for segmentation, pattern extraction, classification, and for generation of predictive models.
for segmentation, pattern extraction, classification, and for generation of predictive models.
Applying advanced data analytics
for segmentation, pattern extraction, classification, and for generation of predictive models.
for segmentation, pattern extraction, classification, and for generation of predictive models.
Extracting hidden patterns from data
and testing their utility (accuracy, sensitivity, specificity) for problem solving.
and testing their utility (accuracy, sensitivity, specificity) for problem solving.
Extracting hidden patterns from data
and testing their utility (accuracy, sensitivity, specificity) for problem solving.
and testing their utility (accuracy, sensitivity, specificity) for problem solving.
Providing informative data features
We allow users monitoring our technology outcome with respect to the expected results, and interpreting its added value in supporting their decisions.
We allow users monitoring our technology outcome with respect to the expected results, and interpreting its added value in supporting their decisions.
Providing informative data features
We allow users monitoring our technology outcome with respect to the expected results, and interpreting its added value in supporting their decisions.
We allow users monitoring our technology outcome with respect to the expected results, and interpreting its added value in supporting their decisions.