A technical overview of the architecture, security model, and execution engine behind Veridata OS.
Identical inputs produce identical outputs. Every pipeline step is versioned and reproducible. No stochastic behavior unless explicitly enabled per tenant, per step.
LLM-enhanced reasoning activates for borderline cases. Confidence scoring, evidence weighting, and full reasoning traces are captured. Deterministic fallback guaranteed.
Every mutation logged. Every output signed. Every connector result tagged with provenance. Complete provenance from ingestion to delivery.
Row-level security policies enforce data boundaries at the database layer. Each tenant's data is cryptographically separated. No shared queries, no cross-tenant leakage.
Cloud KMS signs every pipeline output. Tamper-evident audit trails provide a digital chain-of-custody from ingestion to report delivery.
Electronic signatures, audit trails, and access controls designed for regulatory submission. Every mutation logged with timestamp, user, and before/after state.
PHI handling governed by policy at every layer. Encryption at rest and in transit. Access logging enforced programmatically. Minimum necessary principle applied to every query.
Pipeline steps are independent, typed functions. Compose them into workflows for any clinical domain: variant classification, therapy matching, screening programs, revenue cycle. Add custom steps without modifying the engine. Each step declares its input schema, output schema, and dependencies. The engine validates contracts at composition time, not at runtime.
Real-time enrichment from biomedical knowledge sources. CIViC, ClinVar, ClinicalTrials.gov, OncoKB, gnomAD, PharmGKB, and more. Three-tier resolution: your database first, live API second, curated baseline always available. Every record carries a provenance tag indicating its source, so downstream consumers always know the origin of the data they are acting on.
Failed steps can be retried individually. Downstream steps automatically re-execute with updated inputs. No manual re-runs. No orphaned state. The engine tracks step dependencies as a DAG, so cascade re-execution follows the correct topological order. Partial results are preserved and available for inspection during recovery.
Fully containerized architecture that scales to zero when idle and scales to demand under load. The reference deployment runs on Google Cloud Platform, but every component is cloud-agnostic by design. No vendor lock-in at the infrastructure layer.
Deploy in your cloud project, your VPC, under your security policies. Same platform, same APIs, same compliance guarantees regardless of where it runs. You own the infrastructure, the data, and the outputs it produces.