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veridata health

Technical Specs

Veridata OS is built as a foundational operating system for precision medicine—designed to support regulated clinical, research, and AI-driven workflows at scale.

Rather than delivering isolated applications or point integrations, Veridata provides a unified execution layer that standardizes how biomedical data is ingested, modeled, governed, computed, and operationalized across organizations. Each capability below represents a core building block of the platform, collectively enabling deterministic execution, full lineage, federated operation, and compliance-by-design across the precision medicine lifecycle.

These capabilities define both the current platform architecture and the roadmap for extensible, multi-institution deployment across diagnostics, cell and gene therapy, clinical research, and AI-enabled care.

Microscope

TECH SPECS

The technical foundations behind the platform’s capabilities

    • Platform Core

      • Deterministic Precision Medicine Operating System

      • Unified execution layer spanning ingestion, canonical data modeling, lineage, compute, applications, and AI

      • Purpose-built for regulated biomedical and life sciences environments

     

    • Canonical Schema & Entity Model

      • Unified biomedical canonical data model

      • Extensible schema enabling consistent interpretation and reuse across assays, trials, and workflows

        • Core entities:

        • Patients

        • Samples

        • Assays

        • Molecular features

        • Events

        • Temporal observations

        • Provenance metadata

     

    • Extensibility Framework

      • Composable platform architecture

      • Supports:

        • User-defined schemas

        • Custom compute jobs

        • Plugin extensions

        • External orchestration hooks

    • Multi-Modal Data Ingestion

      • Canonical ingestion pipelines across heterogeneous biomedical data sources

      • QC, normalization, and entity resolution enforced at ingestion

    ​​

    • Supported Data Modalities & Standards

      • Clinical: FHIR / HL7

      • Genomics: FASTQ, BAM, CRAM, VCF

      • Imaging & digital pathology formats

      • LIMS and manufacturing telemetry (CGT)

      • Real-world data (RWE) feeds

    • End-to-End Lineage & Traceability

      • Automatic versioning across raw inputs, transformations, compute execution, and outputs

      • Immutable lineage graph spanning the full data lifecycle

     

    • Provenance Controls

      • Chain-of-identity

      • Chain-of-custody

      • Immutable audit logs

      • Regulatory-grade traceability by design

    • Deterministic Compute Engine

      • Fully reproducible execution across environments

      • Identical inputs produce identical outputs across reruns and deployments

      • Versioned, pinned compute workflows

    ​​

    • Optimized Compute Domains

      • Genomics (HLA, MRD, WGS/WES)

      • Clinical analytics

      • AI inference workloads

      • GPU-accelerated pipelines

    ​​

    • Federated Execution

      • Distributed compute without centralized data movement

      • Federated query and execution across institutions and partners

      • Policy-driven execution and data sovereignty enforcement

    • Agentic AI Workflows

      • AI workflows operate exclusively on harmonized, lineage-verified datasets

      • Full traceability of:

        • Inputs

        • Outputs

        • Inference context

      • Deterministic, auditable AI execution suitable for regulated use cases

    • Clinical & Research Applications

      • Applications as execution surfaces, not standalone silos

      • Directly execute on OS-level data and compute

      • Supported workflows include:

        • Reporting

        • Ordering

        • Trial matching

        • Oncology timelines

        • Operational and manufacturing workflows

    • Identity & Access Control

      • Enterprise IAM enforced consistently across data, compute, and lineage

      • Capabilities include:

        • SSO (SAML / OIDC)

        • RBAC / ABAC

        • Delegated partner access

        • Break-glass controls

    ​

    • Tenancy & Isolation

      • Multi-tenant architecture with strict logical and physical isolation

      • Isolation across:

        • Data

        • Compute

        • Metadata

        • Lineage graphs

      • Designed for multi-sponsor and multi-institution deployments

    • Environment Management

      • Governed Dev, Test, Validation, and Production environments

      • Controlled promotion paths with environment-specific configurations

      • Non-production data masking support

     

    • Validation & Change Control

      • Regulated release management aligned to GxP and 21 CFR Part 11

      • Capabilities include:

        • IQ/OQ/PQ support

        • Version pinning

        • Rollback

        • Validation artifacts and documentation

    • Observability & Telemetry

      • End-to-end operational visibility across ingestion, compute, and applications

      • Includes:

        • Pipeline metrics

        • Job tracing

        • Failure classification

        • Replay and re-execution

        • Cost telemetry hooks

     

    • Data Lifecycle Management

      • Policy-driven lifecycle governance

      • Supports:

        • Retention policies

        • Legal holds

        • Archival and purge semantics

        • Study and assay closeout

    • APIs & Integration Contracts

      • Stable, versioned, contract-driven integration model

      • Interfaces include:

        • REST and gRPC APIs

        • Event-driven streaming

        • Webhooks
          SDKs (Python, TypeScript)

    • Security Architecture

      • Zero-trust, defense-in-depth security model

      • Embedded controls across all platform layers:

        • Encryption at rest and in transit

        • Secrets management

        • Key management (BYOK / HYOK)

        • Immutable audit logs

    ​​

    • Resilience & Disaster Recovery

      • Clinical-grade availability and fault tolerance

      • Capabilities include:

        • Automated backups

        • Regional redundancy

        • Defined RPO / RTO targets

        • Failover testing

    ​​

    • Performance & Scalability

      • Horizontally scalable architecture

      • Supports:

        • Multi-petabyte ingestion

        • High-concurrency compute workloads

        • Backpressure handling

    • Deployment Models

      • Flexible enterprise deployment options:

        • Fully managed

        • Customer-managed

        • Hybrid

    ​​

    • Infrastructure Support

      • Public cloud and hybrid support

      • Kubernetes-based orchestration

      • Infrastructure-as-code enablement

Tech Specs.

  • Deterministic Precision Medicine Operating System

  • Unified execution layer spanning ingestion, canonical data modeling, lineage, compute, applications, and AI

Platform Core

  • Extensible schema enabling consistent interpretation and reuse across assays, trials, and workflows

  • Core entities:Patients, Samples, Assays, Molecular features, Events, Temporal observations, Provenance metadata

Schema & Entity Model

  • Composable platform architecture

  • Supports:

    • User-defined schemas

    • Custom compute jobs

    • Plugin extensions

    • External orchestration hooks

Extensibility Framework

  • Canonical ingestion pipelines across heterogeneous biomedical data sources

  • QC, normalization, and entity resolution enforced at ingestion

Multi-Modal Data Ingestion

  • Clinical: FHIR / HL7

  • Genomics: FASTQ, BAM, CRAM, VCF

  • Imaging & digital pathology formats

  • LIMS and manufacturing telemetry (CGT)

  • Real-world data (RWE) feeds

Data Specs.

  • Automatic versioning across raw inputs, transformations, compute execution, and outputs

  • Immutable lineage graph spanning the full data lifecycle

Lineage & Traceability

  • Chain-of-identity

  • Chain-of-custody

  • Immutable audit logs

  • Regulatory-grade traceability by design

Provenance Controls

  • Fully reproducible execution across environments

  • Identical inputs produce identical outputs across reruns and deployments

  • Versioned, pinned compute workflows

Deterministic Compute

  • Genomics (HLA, MRD, WGS/WES)

  • Clinical analytics

  • AI inference workloads

  • GPU-accelerated pipelines

Optimized Compute

  • Distributed compute without centralized data movement

  • Federated query and execution across institutions and partners

  • Policy-driven execution and data sovereignty enforcement

Federated Execution

  • AI workflows operate exclusively on harmonized, lineage-verified datasets

  • Full traceability of Inputs, Outputs,& Inference context.

  • Deterministic, auditable AI execution suitable for regulated use cases

Agentic AI Workflows

  • Applications as execution surfaces, not standalone silos

  • Directly execute on OS-level data and compute

  • Supported workflows include:Reporting, Ordering, Trial matching, Oncology timelines, & Operational and manufacturing workflows

Clinical & Research Apps

  • Enterprise IAM enforced consistently across data, compute, and lineage

  • Capabilities include:

  • SSO (SAML / OIDC), RBAC / ABAC, Delegated partner access, &Break-glass controls

Identity & Access Control

  • Multi-tenant architecture with strict logical and physical isolation

  • Isolation across Data, Compute, Metadata, & Lineage graphs

  • Designed for multi-sponsor and multi-institution deployments

Tenancy & Isolation

  • Governed Dev, Test, Validation, and Production environments

  • Controlled promotion paths with environment-specific configurations

  • Non-production data masking support

Environment Management

  • Regulated release management aligned to GxP and 21 CFR Part 11

  • Capabilities include:

  • IQ/OQ/PQ support, Version pinning, Rollback, & Validation artifacts and documentation

Validation & Change

  • End-to-end operational visibility across ingestion, compute, and applications

  • Includes Pipeline metrics, Job tracing, Failure classification, Replay and re-execution, & Cost telemetry hooks

Observability & Telemetry

  • Policy-driven lifecycle governance

  • Supports:Retention policies, Legal holds, Archival and purge semantics, & Study and assay closeout

Data Lifecycle Mgmt,

  • Stable, versioned, contract-driven integration model

  • Interfaces include REST and gRPC APIs, Event-driven streaming, Webhooks, SDKs (Python, TypeScript)

APIs & Integration

  • Zero-trust, defense-in-depth security model

  • Embedded controls across all platform layers: Encryption at rest and in transit, Secrets management, Key management (BYOK / HYOK), & Immutable audit logs

Security Architecture

  • Clinical-grade availability and fault tolerance

  • Capabilities include: Automated backups, Regional redundancy, Defined RPO / RTO targets, & Failover testing.

Resilience & Recovery

  • Horizontally scalable architecture

  • Supports: Multi-petabyte ingestion, High-concurrency compute workloads, & Backpressure handling

Performance & Scalability

  • Flexible enterprise deployment options:

    • Fully managed

    • Customer-managed

    • Hybrid

Deployment Models

  • Public cloud and hybrid support

  • Kubernetes-based orchestration

  • Infrastructure-as-code enablement

Infrastructure Support

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Contact us or schedule a demo.

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