Consolidate your genomic, clinical, and phenotypic data into a governed genomic data management platform — then apply AI-driven variant classification, automated VUS interpretation, and cohort-level re-analysis to turn that data into clinical decisions at scale.
Key Capabilities
Six capabilities that turn fragmented genomic data into a governed, AI-powered interpretation engine — designed for clinical labs, precision medicine programmes, and pharma R&D teams running large-scale variant analysis.
Consolidates genomic, clinical, and phenotypic data into a governed data layer — variant files, clinical annotations, and omic datasets unified on a shared identifier with role-based access, consent tracking, and immutable provenance records.
ML-assisted ACMG/AMP classification — evidence aggregated from gnomAD, ClinVar, REVEL, CADD, SpliceAI, and internal lab history — surfaced with confidence scores and human-readable rationale so clinical teams review, verify, and sign off.
VUS interpretation software with scheduled cohort-level re-analysis — automatic reclassification workflows triggered by evidence threshold changes, with notification to ordering clinicians on every reclassification event.
Query across your entire variant dataset — cohort-level frequency, co-occurrence, phenotype correlation, and population stratification — using a genomics data lake architecture built for the scale and dimensionality of clinical variant repositories.
Integrates WGS output, RNA-Seq expression data, proteomic profiles, and clinical phenotypes into a unified multi-omic data analysis layer — enabling cross-omic biomarker discovery and precision medicine programme analytics.
Feature engineering pipelines, training environment on AWS SageMaker / GCP Vertex AI, experiment tracking with MLflow, and clinical deployment of pathogenicity prediction, tumour classification, and polygenic risk scoring models.
Technical Architecture
Built on HIPAA-compliant genomic data infrastructure — VPC-isolated, KMS-encrypted, with immutable audit logs per classification event. Deployable on AWS, GCP, or Azure. Every AI output includes explainability metadata (SHAP values, evidence weights) so clinical teams never accept a classification on faith.
Connected Platforms
The upstream pipeline execution layer — producing the variant calls this platform classifies and re-analyses.
View Platform →Full-lifecycle clinical platform — the AI Analytics module sits within the interpretation layer of the complete clinical workflow.
View Platform →Cross-omic integration platform — feeds transcriptomic, proteomic, and phenotypic data into the unified multi-omic analysis layer.
View Platform →How does AI-driven variant classification differ from manual ACMG interpretation?
Manual ACMG interpretation requires the interpreter to retrieve population frequency data, functional predictions, splice impact scores, literature citations, and internal lab history for every variant — a process that takes 15 to 45 minutes per variant depending on complexity.
The AI-driven variant classification platform automates that evidence retrieval and aggregation, presenting the interpreter with a pre-assembled evidence dossier, a suggested ACMG classification tier, a confidence score, and the specific criteria that contributed to the suggestion. The interpreter reviews, adjusts if needed, and signs off.
For high-volume labs running large gene panels or population-scale WGS programmes, this reduces interpretation time per variant by 60–70% while maintaining full clinical oversight and an auditable decision trail. VUS re-analysis runs automatically as new evidence accumulates — ensuring reclassification happens when the evidence supports it, not when someone remembers to check.
What data sources does the platform aggregate for variant interpretation?
The automated variant interpretation platform aggregates evidence from:
For precision medicine software development use cases, the platform also integrates phenotype data via HPO terms captured at order entry, enabling phenotype-driven variant prioritisation that surfaces the most clinically concordant variants before the interpreter opens the case.
Get Started
Tell us your variant volume, your current interpretation tooling, and your biggest throughput bottleneck.
See AI-assisted classification on a live variant dataset
