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From fragmented raw data to a governed, AI-ready genomic data management platform — we build the infrastructure that turns your omic data from an operational liability into a precision medicine asset.
Talk to Our Data and AI Engineering Team →Why It Breaks Down
These are not edge cases — they are the default state of most genomics data environments before a purpose-built platform replaces the patchwork.
Variant files in object storage. Clinical records in the EHR. Phenotype data in spreadsheets. No single layer where any of it connects.
Genomics, transcriptomics, and proteomics datasets live in separate tools with incompatible formats and no shared identifier layer.
ML models never leave the prototype stage because the underlying data is inconsistent, unlabelled, or lacks the feature engineering needed for clinical-grade training.
Cohort-level re-analysis and VUS reclassification require querying across thousands of past cases — which is impossible without a structured, queryable genomic data warehouse.
Capabilities
We engineer every layer of the clinical genomics data stack — from raw omic ingestion through governed analytics to production AI. Six capabilities. One connected platform.
A genomic data management platform is the foundation on which everything else depends. We design and build a governed genomics data lake architecture that consolidates variant data, clinical records, phenotype information, and omic files from across your entire system landscape into a single queryable layer, with access controls, lineage tracking, and audit trails that meet regulatory requirements.
Talk to Our Expert →A data lake holds the raw material. A genomics data warehouse makes it analytically usable. We build ETL pipelines and data warehouse architectures specifically designed for whole-genome sequencing data management and large-scale genomic analytics — where standard data engineering approaches fail due to the volume, dimensionality, and biological structure of omic data.
Schedule a Call →Single-omic analysis answers single questions. Multi-omic data analysis answers the ones that matter — which molecular mechanisms drive this phenotype, which biomarkers stratify this patient population, which drug targets emerge when genomics, transcriptomics, and proteomics are read together. We build multi-omic analysis platforms for cancer research, rare disease genomics, and precision medicine programmes that require cross-omic biological insight.
Let’s Talk →The most common reason genomics AI projects fail is not the model — it is the data preparation that precedes it. We engineer the upstream infrastructure that makes AI possible: omic data preparation pipelines, feature engineering workflows, and training environments built to produce clinically relevant, reproducible outputs.
Talk to Our Expert →We take organizations from raw proprietary omic datasets to clinically deployed models — managing the full ML lifecycle so your team focuses on the science and the clinical question, not the engineering overhead.
Schedule a Call →Manual ACMG variant classification is the throughput bottleneck of clinical genomics. We build AI-driven variant classification platforms that accelerate interpretation without removing clinical oversight — applying machine learning to surface evidence, suggest classifications, and prioritise the variants that need human attention most.
Evidence aggregation across ClinVar, gnomAD, in-silico tools, and internal lab history — surfaced at the variant level with confidence scores.
Systematic re-evaluation of variants of uncertain significance as new evidence accumulates, with automated reclassification workflows and notification to ordering clinicians.
HPO term integration to rank variants by clinical concordance before the interpreter opens the case.
Every classification suggestion includes the evidence basis, the weight of each criterion, and a human-readable rationale — ensuring clinical teams can trust, verify, and sign off on AI-assisted interpretations.
Who We Help
Our genomic data management and AI engineering services support organizations where data depth and analytical scale are the core competitive advantage.
Population genomics programmes and biobanks accumulating whole genome sequencing data across tens of thousands of participants — needing the data infrastructure to make that asset analytically and scientifically productive.
Drug development organizations running genomic patient stratification, biomarker discovery, and companion diagnostic development programmes that require AI-ready data infrastructure and ML model training on proprietary omic datasets.
Companies building AI-powered genomics products — variant interpretation tools, polygenic risk platforms, clinical decision support engines — who need a data and ML engineering partner to build the infrastructure their product sits on.
Platforms
The data and AI infrastructure we build connects directly into these NonStop platforms:
The production platform for AI-driven variant classification, VUS re-analysis, and cohort querying — built on the data infrastructure described on this page.
View Platform →Cross-omic insights platform integrating genomics, transcriptomics, and proteomics with clinical data — for biomarker discovery, cancer research, and precision medicine.
View Platform →The upstream pipeline execution layer — producing the variant calls and omic outputs that feed into your data platform.
View Platform →FAQ
A genomic data management platform is a purpose-built data infrastructure layer that consolidates variant data, omic files, clinical records, and phenotype information from across your system landscape into a single governed, queryable environment. Unlike a standard database, it handles the scale (billions of variants across thousands of samples), the biological structure (reference genome builds, annotation versioning, allele representation), and the regulatory requirements (access control, consent tracking, immutable provenance) specific to genomic data. We architect these systems using genomics data lake architecture on AWS, GCP, or Azure, with ETL pipelines, query engines, and data governance tooling built specifically for omic data rather than adapted from general-purpose enterprise data infrastructure.
HIPAA compliance for a cloud-native genomics data platform requires architectural decisions at every layer, not just security controls bolted on at the end. We design VPC-isolated compute and storage environments, enforce encryption at rest and in transit using customer-managed KMS keys, implement IAM policies that follow least-privilege principles, log every data access event to immutable audit trails, and ensure PHI never flows through uncontrolled paths between services. On AWS, this means configurations across S3, Lake Formation, Glue, Athena, and SageMaker. On GCP, it means equivalent controls across Cloud Storage, BigQuery, Vertex AI, and the VPC Service Controls perimeter. We deliver a compliance architecture document alongside every platform build, covering controls mapped to HIPAA Security Rule requirements.
In production, an AI-driven variant classification platform sits within the interpreter’s existing workflow, not as a replacement for clinical judgement but as a layer that does the evidence assembly work before the interpreter opens a case. When a variant enters the interpretation queue, the system automatically aggregates population frequency data from gnomAD, functional predictions from REVEL and CADD, splicing impact from SpliceAI, literature citations from ClinVar and ClinGen, and internal lab classification history, then applies a trained model to suggest an ACMG classification tier with a confidence score and an evidence breakdown. The interpreter reviews, adjusts, and signs off. The output of every interaction — the suggestion, the review, the final classification — is recorded in the audit trail. VUS re-analysis runs on a scheduled basis as new evidence accumulates, with automated reclassification and clinician notification workflows triggered by evidence threshold changes.
Yes, multi-omic analysis platforms for cancer research are one of our most common engagements in this solution area. Cancer genomics generates data across multiple omic layers simultaneously: somatic mutations from whole genome or panel sequencing, gene expression changes from RNA-Seq, copy number alterations, methylation profiles, and, in some settings, proteomic data from mass spectrometry. A useful multi-omic analysis platform for cancer research integrates all of those layers on a shared patient and sample identifier, enables pathway and network analysis across omic types, provides interactive cohort exploration for research scientists, and supports the feature engineering and model training pipelines needed for tumour classification, drug response prediction, and biomarker discovery. We build these platforms for academic cancer centres, translational research groups, and pharma R&D programmes, using open-source frameworks (Hail, MOFA+, DIABLO) where appropriate and custom-built layers where specific analytical or operational requirements demand it.
Tell us what you are trying to do with your data — query it, train on it, or automate interpretation. We will come back with a scoped approach.