What challenges did Natera face before AI implementation?

Scale high-volume genomic diagnostic operations while maintaining secure handling of private patient data. Reduce data silos and improve internal data sharing for analytics and decision-making. Accelerate time-consuming research and clinical insight generation from unstructured clinical documents.

What was the impact of the AI solution?

Natera nearly doubled data extraction, increasing extracted elements per test document from 25 to 46. The company reported a 150% reduction in data processing costs. The workflow helped support detection of disease recurrence up to 9 months earlier than standard imaging. The platform scale supported over 54,000 tests and 60,000 pipelines weekly across lab locations.

Rank#220of 3,449

Source

Evidence: Medium50/100

Natera transforms patient care with generative AI on AWS

Use case typeMedical document automationUpdated Jun 13, 2026

Natera, a global cell-free DNA testing company focused on oncology, women’s health, and organ health, modernized its data and analytics platform on AWS to support high-volume diagnostic operations and secure handling of patient data. The implementation aimed to reduce data silos, improve access to clinical and genomic data, and accelerate extraction of information from unstructured documents to support precision medicine and earlier detection of cancer recurrence.

Organization: Natera
Industry: Healthcare
Location: United States
Published: April 2026

Reported outcomes

−150%

costCost savings

Strategic outcomes

New product / capabilityAutomated genomic and clinical document processingBetter decisions & insightImproved clinical insight generationCustomer experience & trustEnabled earlier disease recurrence detectionScale & capacitySupported high-volume diagnostic operations

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Cost: 150% decrease

AWS Solutions Case StudyApr 29, 2026Case studyInferred claimMedium evidence strength

The company reported a 150% reduction in data processing costs.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Natera
Provider: AWS
Maturity: Unknown
Linked source: AWS Solutions Case Study

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 5

1Genomic diagnostics
2Document processing
3Knowledge retrieval

Scale high-volume genomic diagnostic operations while maintaining secure handling of private patient data.
Reduce data silos and improve internal data sharing for analytics and decision-making.
Accelerate time-consuming research and clinical insight generation from unstructured clinical documents.

Natera built an AI-first data infrastructure on AWS using AWS HealthOmics to automate computational processing of massive cfDNA datasets.
The company used AWS Step Functions to orchestrate workflows from dataset ingestion through output archival with traceability.
Amazon Textract extracted structured and unstructured content from clinical documents, and Amazon Bedrock with Claude 3.5 Sonnet generated embeddings and performed document-level parsing and understanding.
Embeddings were stored in Amazon RDS with vector search for semantic retrieval, and Amazon SageMaker Unified Studio was used to manage end-to-end analytics and machine learning workflows with governed data access.
Amazon QuickSight/QuickSuite-style reporting and collaboration capabilities were used to combine and analyze data from multiple sources.

Technologies

AWS HealthOmics Amazon Bedrock Amazon Textract AWS Step Functions Amazon SageMaker Unified Studio Amazon RDS Amazon QuickSuite

Natera nearly doubled data extraction, increasing extracted elements per test document from 25 to 46.
The company reported a 150% reduction in data processing costs.
The workflow helped support detection of disease recurrence up to 9 months earlier than standard imaging.
The platform scale supported over 54,000 tests and 60,000 pipelines weekly across lab locations.

Architecture

Clinical and genomic data is processed on AWS with AWS HealthOmics handling large-scale cfDNA workflows. AWS Step Functions orchestrates the workflow. Amazon Textract extracts text and layout from unstructured clinical documents. The extracted content is chunked and analyzed in Amazon Bedrock using Claude 3.5 Sonnet to generate embeddings and perform document parsing. Embeddings are stored in Amazon RDS with vector search for semantic retrieval. Amazon SageMaker Unified Studio supports governed analytics and data product sharing across producers and consumers.

Sources & evidence1

Evidence: Medium50/100Evidence strength