Life Sciences

AWS infrastructure for GxP environments and petabyte science.

Life sciences workloads on AWS combine two unusual demands: regulator-grade change management (GxP, 21 CFR Part 11) and HPC-scale data pipelines that don't fit anywhere else. We build environments that survive both audit and bioinformatics throughput.

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Why this is different on AWS

The life-sciences-on-AWS reality

  • GxP validation: every change to a production environment is documented, reviewed, and reproducible
  • 21 CFR Part 11 electronic records — immutable audit, signature workflows, and retention policies
  • Bioinformatics workloads scale to thousands of cores in bursts — Batch and ParallelCluster, not always-on fleets
  • Petabyte-scale genomic and imaging datasets demand storage tiering (S3 Intelligent-Tiering, Glacier Deep Archive)
  • Clinical trial systems often need geographic data residency (EU, Japan, US) per regulator
  • HealthOmics is the newest AWS tool — purpose-built for genomic workflows at production scale

GxP without the bureaucracy

GxP doesn't have to mean change tickets that take three weeks. We design IaC pipelines where the validation evidence is generated automatically by the same pipeline that ships the change.

Terraform plans + approvals + Config rules + CloudTrail = the change history a CSV auditor will accept.

Where this applies

Where we plug in

Biotech

Research computing and lab data

Bioinformatics pipelines (GATK, Nextflow, Snakemake) on AWS Batch and ParallelCluster, lab data lakes in S3 with metadata-driven access, and HealthOmics-native workflow design.

Pharmaceuticals

Clinical trials and manufacturing

EDC systems, eCRFs, clinical data warehouses, and pharmacovigilance pipelines. Manufacturing telemetry from production lines into AWS IoT and time-series stores. GxP scope, every layer.

Medical Devices

SaMD and connected devices

FDA Software as a Medical Device (SaMD) platforms, device telemetry ingestion, and OTA update infrastructure. Cybersecurity controls aligned to the FDA's premarket guidance for medical device cybersecurity.

AWS services we lean on

The life sciences stack we build with

  • AWS HealthOmics for genomic workflow orchestration, storage, and annotation
  • AWS Batch + ParallelCluster + FSx for Lustre for HPC-class bioinformatics pipelines
  • S3 Intelligent-Tiering and Glacier Deep Archive for raw sequencing reads and long-tail trial data
  • AWS Config + CloudTrail Lake for the immutable change history GxP auditors expect
  • Lake Formation + LF-Tags for fine-grained access to clinical and research data
  • SageMaker + Bedrock for drug discovery models, target identification, and clinical trial enrichment
How we engage

Solutions that map to life sciences work

Well-Architected Review

Pre-submission audit before the regulator gets eyes on your validation packet. Find the gaps now, not during the FDA's inspection.

AWS Modernization

Bioinformatics pipelines off ad-hoc EC2 fleets and onto Batch/HealthOmics. Lab systems off on-prem and into validated AWS environments.

AI Solutions on AWS

Bedrock for protein-language models, SageMaker for custom predictors, and inference infrastructure that handles real research throughput.

Science at AWS scale, audited like a regulator's watching.

Because they are. We build AWS environments that hold up to the regulator's questions and to your scientists' workloads.

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