.png){kind=small}
VE3 built an AI-native, federated-ready research platform to help a leading genomics institution unlock insight from 130 PB(Petabyte) of genomic data, without compromising security, performance, or trust.
A growing mix of bioinformaticians, researchers, and clinicians needed very different ways to interact with the same data. At the same time, the platform needed to scale from ~1,200 to 5,000+ researchers, while improving usability and reducing operational overhead.
Discovery, analysis, and governance were spread across multiple tools, slowing workflows, and increasing complexity.
Researchers spent more time preparing, moving, and reconciling datasets than analyzing them, limiting the value of elastic cloud compute.
Strict data sovereignty requirements made it difficult to balance flexible cloud compute with zero-copy access and full auditability. Alongside strict prevention of data exfiltration and full auditability requirements.
GenomiX is an AI-native platform built around real user needs, offering coders full CLI and API access, while enabling clinicians and non-coders to use intuitive, natural language tools. This approach overcomes a key legacy challenge—making data easier to discover and understand for faster research.
VE3 implemented a dual-layer architecture separating control and analysis functions. The Control Plane manages orchestration and identity without accessing sensitive data, while the Analysis Plane runs within the client’s AWS environment, ensuring data stays secure.
This approach ensures complete data sovereignty, eliminates unnecessary data movement, and delivers high-performance compute close to the data. Built for a federated future, the platform embeds global standards such as DRS, Data Connect, WES, and Passports to enable secure, cross-environment collaboration without duplicating data.
Unified Research Workspace
One environment for genomics, clinical, multi-omics, and imaging data with built-in exploration and visualization.
AI-Powered Discovery
Natural language queries allow researchers to ask questions directly and generate cohorts instantly.
Integrated MLOps
End-to-end machine learning workflows including experiment tracking, model management, and AutoML.
Reproducibility by Design
Every visual workflow or cohort can be exported into Python or R for full reproducibility.
Zero-Trust Security
All data access is governed by the user’s own permissions, ensuring strict compliance and control.
Cost & Usage Transparency
Built-in cost tracking and controls at user and project level, enabling visibility, governance, and future billing readiness.
.png)
GenomiX enables a shift from infrastructure-heavy research environments to scalable, discovery-first platforms. It creates a foundation not just for managing genomic data, but for accelerating national-scale and globally connected research.
.png)
