HEALTH INTELLIGENCE / PERSONALIZED INSIGHT
Connect genomic, multi-omics, and lifestyle data to turn data into health information that can be tracked over time.
01 / END-TO-END VALUE CHAIN
The wet lab generates test data, while the dry lab processes and interprets it. Seven steps connect testing, analysis, and ongoing health management.
Support sample processing, library preparation, and workflow traceability with automation platforms.
Obtain genomic data through WGS; sequencing depth is confirmed according to the service plan and quality specifications.
Process sequencing data using acceleration approaches such as FPGA/ASIC computing to generate variant data for downstream interpretation.
Classify and interpret variants according to applicable professional guidelines, such as ACMG/AMP.
Integrate genetic information and risk assessment for interpretation by professional teams.
Combine proteomic, metabolomic, and lifestyle data to observe changes over time.
Professional teams review the combined results and discuss nutrition, lifestyle, and follow-up plans.
Reference planning includes 30× WGS and hardware-accelerated VCF output in approximately 30 minutes. Actual depth, turnaround, and functionality depend on samples, system configuration, and validation conditions.
02 / PLATFORM ARCHITECTURE
From the data foundation, models, and knowledge retrieval to health agents and personal interfaces, these layers form an integrated health information architecture.
Connect the user's health information experience through personalized health content, AI consultation assistants, nutrition recommendations, a health marketplace, and a disease encyclopedia.
The whole-genome AI platform focuses on WGS-based genetic risk assessment and prevention directions; the precision health system combines proteomics, metabolomics, and ongoing monitoring.
Evaluate cloud models, such as ChatGPT, Claude, and Gemini, or local open-source models for each deployment. Combine RAG, Skills, and medical knowledge retrieval to support information organization and professional workflows.
Integrate data from different sources and time points to establish a foundation for subsequent analysis, tracking, and interpretation.
The illustration shows platform architecture and service planning. Interfaces, models, data sources, and features are confirmed according to the actual solution and deployment.
03 / HEALTH DIGITAL TWIN
Integrate five categories of data through health agents for organization and analysis, supporting the different needs of physicians, health advisors, and users.
Organize health and test information to support clinical interpretation and decision-making.
Help plan and track lifestyle and nutrition actions.
Present personal data, health content, and tracking information in an accessible format.
FROM INSIGHT TO ACTION
ESSENSE Clinic is a real-world setting for health agents, extending from raw data, annotation, and interpretation to health analysis and recommendations. Applicable services and follow-up approaches depend on individual circumstances and professional assessment.
START A CONVERSATION
Discuss health agents, data integration, and collaboration opportunities with ESSENSE AI.
Health information and AI analysis are for professional reference and do not replace a physician's diagnosis or treatment. Actual services, data protection, and functionality depend on the agreed solution.
