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Data mesh and data products streamline data management and collaboration, enhancing the scalability, quality, and versatility of analysis and generative AI applications while improving security and reducing latency. Tag.bio technology simplifies data preparation, supports iterative development, and ensures reliable, high-quality data for generative AI models, ultimately leading to more effective and efficient AI systems.
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Leverage any data source (or combination of sources) to instantiate governed, versioned, containerized, git-backed data products.
Use built-in enterprise CI/CD process to deploy containerized data products in your secure compute environment (AWS, Azure, GCP, Hybrid, On-prem).
Harmonized data product APIs facilitate universal, governed access via frontend applications and data science environments (IDEs, notebooks).
Data challenges are everywhere in HLS, and data infrastructure must be flexible enough to handle the wide variety and large volumes of collected data. There are still many unmet needs across the biomedical domain: secure collaboration, reproducible data science, emerging data types, specialized algorithms, and multi-modal integration. Tag.bio's technology addresses all of these challenges with simplicity and transparency, delivering high-value applications, specialized algorithms, generative AI, and high-quality data frames to researchers, business units, physicians, data scientists, and analytics dashboards.
Multi-modal data sources are merged into a simplified, harmonized, versioned data model - for example, a patient registry with medical history and genomics, or a phase 2 clinical trial. Ontologies and controlled vocabulary mappings ensure quality in the model for downstream use. Data modeling is turnkey (e.g. OMOP, CDISC), and customizable for novel and emerging data sources (e.g. scRNA-Seq).
Tag.bio data products are designed for use of the data - not storage of the data. Use cases are defined by stakeholders (domain experts), ingestion and modeling is performed by data engineers, and algorithms, workflows, and advanced AI components are integrated as Apps by data scientists. This is all facilitated within the Tag.bio platform, which provides simple-yet-powerful tooling for design, testing, deployment, evaluation, and improvement of data products.
Once published, each data product is equipped with specialized Apps for domain experts to repeatedly ask and answer questions via point-and-click, for data scientists to extract high-quality data frames into R or Python, for use in AI and Generative AI algorithms, and for delivery to dashboards and other analysis software.
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There are many facets to scalability of data systems - Tag.bio's data mesh covers them all - from the micro scale within data products (optimized memory use, low-code templates, sharable functions & algorithms) to the macro scale across data products (load balancing, orchestration, distribution of volume, decentralized & composable data products, CI/CD, testing, etc.).
Tag.bio's data mesh is deployed with a fully-managed suite of design, deployment, testing, orchestration, governance, and analytics components. Researchers can use the web frontend to ask data questions to the mesh via point-and-click, data scientists can use the integrated Jupyter notebook to work with data in the mesh, and cloud engineers can manage deployment of data products and the scaling of the mesh across any cloud.
Tag.bio's data mesh will begin to deliver value after deployment of its first data product, which can happen as quickly as one day, for a turnkey data source (OMOP, CDISC). Additional value is then derived incrementally with each data product deployed into the mesh and as more users are on-boarded to the platform. Compounding value can be achieved as data products are cross-analyzed, yielding novel answers to questions that could never be answered before.
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Accelerate AI development in Healthcare and Life Sciences using Tag.bio. The platform offers tools for building, fine-tuning, and deploying AI models, including support for commercial (GPT-4, Claude-2, PaLM-2) and open-source (Mistral AI, Llama-2, Falcon-7b) foundational models
Use LLM to enhances biomedical data analysis by generating insights, automating tasks, and uncovering hidden patterns within data products.
Deploy LLM agents to automate healthcare workflows, integrate data analysis, and guide research with RAGs, propelling life science discoveries.
Fine-tune LLMs to unlocks personalized medicine, predicts disease, and accelerates drug discovery in healthcare and life sciences.
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Clinical Trials are at the core of the Pharmaceutical industry. Success or failure of a trial can dramatically change how a large pharma operates, and for smaller companies, it means life-or-death. Identifying heterogeneity & discriminating biomarkers, toxicity and dosage effects, and potential positive or negative interactions are all essential data tasks - within and across trials. In collaboration with a top 10 pharma, Tag.bio's platform automatically instantiates harmonized data products from various sources (incl. CDISC and omics) to make them instantly usable and interoperable.
In collaboration with Parkinson's Foundation, the Tag.bio platform provides a securely accessible and distributable data product (from Redcap) which enables discovery of disease-related DNA-Seq variants, identification of patient cohorts by genotype, medical history, and geographic region, and deep informatics research (e.g. GWAS) for both researchers (point-and-click), data scientists (R & Python data frames), and AI algorithms.
High-value datasets are generated, published, and utilized at every layer of the pharmaceutical value chain (i.e. preclinical, clinical, RWE, pharmacovigilance, safety, health economics, marketing). At leading pharmaceutical companies, the Tag.bio data mesh provides rich, harmonized data products for each of these layers - enabling applications, ad-hoc analysis, and generative AI questions to be asked across one or more of them - providing a holistic, data-driven view of a therapeutic's overall impact (including weaknesses and market opportunities).
The University of California, San Francisco Medical Center (UCSF) is dedicated to improving patient care and improving population health outcomes, while minimizing unnecessary activities and excessive costs. UCSF utilizes Tag.bio data products spanning critical EHR and cost-accounting data sources - giving physicians the tools to find over $10M in cost savings while improving standards of care.
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