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We build AI/ML cloud-scale bioinformatics applications to analyze multi-omicdata sets and accelerate biological insights and drug discovery.
Our platform, ERGO, allows scientists to quickly discover new insights from exponentially growing biomedical datasets to make more informed decisions. These insights can be used to put assay results in context, validate targets, support pathway analysis, gene-to-disease relationship discovery, and advance drug development pipelines.
Cloud-scale bioinformatic and NLP solutions for faster drug development
We build data science and data engineering platforms to store and process clinical trial data to discover biomarkers to stratify patients, assess therapeutic response, optimize treatment, and predict participant behavior.
Our solutions incorporate the many types and sources of data captured inclinical trials such as multi-omics, sensors, EEG, ECG, actigraphy, gaze, video, voice, digital therapeutic applications, clinical data, and patient reported outcomes.
Solutions for complex,
multi-modal biomarker discovery
We create digital strategies, solutions, and workflow automations for healthtech, medical devices, and Software as a Medical Device (SaMD) to accelerate time to market.
We develop a digital strategy that considers the data, algorithms, architecture, integrations, and patient and provider experience. We design mobile applications to optimize patient engagement and ensure the capture of valuable real-world data. We train predictive models to monitor health conditions and generate alerts when interventions are required. We design and implement cloud infrastructure and healthcare workflow automation that supports MLOps considerations including versioning, retraining, deployment, and monitoring.
Full stack AI applications for healthtech, medical devices, and SaMD
We design and implement data science platforms and cloud architecture to support the data science lifecycle and enable scalable model performance for production applications.
We have experience with the major cloud vendor tools and with cloud native, open source tools to connect data scientists to scalable compute and storage resources via Kubernetes. Our solutions allow easier design, creation, versioning, and deployment of complex data pipelines. To support full MLOps solutions, we can incorporate custom model monitoring and automated retraining approaches. We offer managed services to support our clients that have not yet built their Data Engineering and ML Engineering teams.
Data science platforms and cloud architecture to support the datascience lifecycle and enable scalable model performance
We build AI/ML cloud-scale bioinformatics applications to analyze multi-omicdata sets and accelerate biological insights and drug discovery.
Our platform, ERGO, allows scientists to quickly discover new insights from exponentially growing biomedical datasets to make more informed decisions. These insights can be used to put assay results in context, support pathway analysis and gene-to-disease relationship discovery, and advance drug development pipelines.
We build data science and data engineering platforms to store and process clinical trial data to discover biomarkers to stratify patients, assess therapeutic response, optimize treatment, and predict participant behavior.
Our solutions incorporate the many types and sources of data captured in clinical trials such as multi-omics, sensors, EEG, ECG, actigraphy, gaze, video, voice, digital therapeutic applications, clinical data, and patient reported outcomes.
We create digital strategies and solutions for connected medical devices and Software as a Medical Device (SaMD) to accelerate time to market.
We work with connected medical device and SaMD teams to develop a digital strategy that considers the data, algorithms, architecture, integrations, and patient and provider experience.
We design mobile applications to optimize patient engagement and ensure the capture of valuable real-world data. We train predictive models to monitor health conditions and generate alerts when interventions are required. We design and implement cloud infrastructure that supports MLOps considerations including versioning, retraining, deployment, and monitoring.
We design and implement data science platforms and cloud architecture to support the data science lifecycle and enable scalable model performance for production applications.
We have experience with the major cloud vendor tools and with cloud native, open source tools to connect data scientists to scalable compute and storage resources via Kubernetes. Our solutions allow easier design, creation, versioning, and deployment of complex data pipelines. To support full MLOps solutions, we can incorporate custom model monitoring and automated retraining approaches. We offer managed services to support our clients that have not yet built their Data Engineering and ML Engineering teams.
We use state-of-the-art methods to extract domain-specific entities and learn semantic relationships from unstructured text data (scientific literature, patents, clinical trials, etc.)
We engineer video, voice and text analytics products to quantify human behavior and extract digital biomarkers to provide information about engagement, emotion, mental state and physical health.
We are domain experts in computational biology with technical expertise in biomarker discovery, genomics / GWAS, mechanism of action (MOA), multi-omics and pathway studies.
We engineer custom pipelines for feature extraction and modeling across a wide variety of data types (customer, device/sensor, images, etc.). Our team has expertise in time series forecasting, computer vision, and other statistical machine learning methods including deep learning.
Our work supports Pre-Clinical, Clinical, Digital Health & Production Data Science
