Our core competencies lie in the intersection between technology and healthcare. Our services portfolio includes projects in healthcare data harmonization and analysis, development of AI-powered mobile healthcare applications and cloud-based medical solutions.
We prove that advances in AI, big data and machine learning can provide new healthcare opportunities.
Digital Health management and data harmonizationData integration brings together data from different sources like electronic health records (EHRs) and medical devices to create a complete view of patient information. We can analyze, clean and harmonize your healthcare data as well as align it with standard medical data models such as OMOP CDM.
Ontology developmentDeveloping ontologies using common clinical data is a very important solution to recording healthcare patient history, using medical guidelines, and service accountability, and helping build stronger systems and higher interaction of information in healthcare. Our team was part of the OHDSI community from its early days, and have developed multiple ontologies for various use-cases. 
Healthcare data scienceOur team is highly experienced in conducting observational research within the OMOP CDM ecosystem, covering the mapping of source data, building concept sets and defined cohorts, characterizing populations using descriptive statistics, estimating and predicting population-level effects, and generating publishable evidence. Our full lifecycle support includes proposing design changes, implementing improvements, and concluding complete research using the developed enhancements at all stages. With our expertise in OMOP CDM, medical domain, and data science, we are equipped to guide and support you throughout transforming raw data into completed research.
Observation studies support and supervisionOur team has extensive expertise in working with healthcare data within the OMOP ecosystem, from initial conversion to generating evidence. Full lifecycle support from proposing design changes to concluding complete research at all stages using developed improvements.
Medical data labelingIn machine learning, data labeling is the process of identifying raw data (images, text files, videos, etc.) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it. We have years of experience in segmenting and labeling all anatomical structures using any type of medical image (e.g. MRI, CT etc.).
AI/ML models for medical tasksWe have a team of CV and NLP researchers and engineers who have successfully built computer vision and machine learning algorithms for image processing and recognition. As well as the NLP models for claims and medical records processing. We can not only train, and fine-tune your model, but create from scratch an end-to-end CV-powered solution tailored for your task.
Medical data ETLOur team has extensive experience working on healthcare projects that utilize streaming ETL processes and we specialize in optimizing performance for these workflows. By leveraging ETL, healthcare organizations can gain insights into patient care, operational efficiency, financial performance, and other critical areas. Our expert team of data engineers can help you manage your data workflows seamlessly and efficiently.
Digital Health management and data harmonizationData integration brings together data from different sources like electronic health records (EHRs) and medical devices to create a complete view of patient information. We can analyze, clean and harmonize your healthcare data as well as align it with standard medical data models such as OMOP CDM.
Ontology developmentDeveloping ontologies using common clinical data is a very important solution to recording healthcare patient history, using medical guidelines, and service accountability, and helping build stronger systems and higher interaction of information in healthcare. Our team was part of the OHDSI community from its early days, and have developed multiple ontologies for various use-cases. Healthcare data scienceOur team is highly experienced in conducting observational research within the OMOP CDM ecosystem, covering the mapping of source data, building concept sets and defined cohorts, characterizing populations using descriptive statistics, estimating and predicting population-level effects, and generating publishable evidence. Our full lifecycle support includes proposing design changes, implementing improvements, and concluding complete research using the developed enhancements at all stages. With our expertise in OMOP CDM, medical domain, and data science, we are equipped to guide and support you throughout transforming raw data into completed research.
Observation studies support and supervisionOur team has extensive expertise in working with healthcare data within the OMOP ecosystem, from initial conversion to generating evidence. Full lifecycle support from proposing design changes to concluding complete research at all stages using developed improvements. Medical data labelingIn machine learning, data labeling is the process of identifying raw data (images, text files, videos, etc.) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it. We have years of experience in segmenting and labeling all anatomical structures using any type of medical image (e.g. MRI, CT etc.).
AI/ML models for medical tasksWe have a team of CV and NLP researchers and engineers who have successfully built computer vision and machine learning algorithms for image processing and recognition. As well as the NLP models for claims and medical records processing. We can not only train, and fine-tune your model, but create from scratch an end-to-end CV-powered solution tailored for your task.
Medical data ETLOur team has extensive experience working on healthcare projects that utilize streaming ETL processes and we specialize in optimizing performance for these workflows. By leveraging ETL, healthcare organizations can gain insights into patient care, operational efficiency, financial performance, and other critical areas. Our expert team of data engineers can help you manage your data workflows seamlessly and efficiently.
