What challenges did vivaLAB face before AI implementation?

Ingesting and analyzing 7-15 GB of raw biometric data per user created computing and storage bottlenecks. Annotating every gene took up to eight weeks, slowing consumer engagement. The team needed a secure, compliant way to scale precision-health reporting.

What was the impact of the AI solution?

Reduced speed-to-science for genomics annotation from eight weeks to under four weeks. Reduced data maintenance costs by 30%. Reduced bioinformatics run costs from $1,500 to $400 per report.

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Source

GCP

vivaLAB: Gemini Enterprise Agent Platform + BigQuery ML to accelerate genomics annotation and humanize biomarker insights

Use case typeHealthcare analyticsUpdated Jun 13, 2026

vivaLAB is a precision-health platform that ingests multi-omic and real-world biomarker data to produce personalized, longitudinal health insights. The company used Google Cloud serverless infrastructure with BigQuery, BigQuery ML, Gemini Enterprise Agent Platform, Gemini, Cloud Run, Firebase, and Cloud Storage to unify data, detect non-linear correlations, and translate clinical biomarkers into plain-English narratives.

Organization: vivaLAB
Industry: Healthcare
Location: United States
Published: May 2026

Reported outcomes

−30%

data maintenance cost reductionCost savings

8 weeks to under 4 weeksgenomics annotation turnaround1,500 USD to 400 USD per reportbioinformatics run cost per report

Strategic outcomes

New product / capabilityGenerated plain-English biomarker narrativesNew product / capabilityUnified multimodal health data analysisRisk & complianceBuilt secure, compliant reporting pipelineSpeed & agilityAccelerated genomics annotation workflow

Catalog median for cost savings deployments: −41% across 333 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 3

1Conversational AI
2Data Analytics
3Healthcare AI

Ingesting and analyzing 7-15 GB of raw biometric data per user created computing and storage bottlenecks.
Annotating every gene took up to eight weeks, slowing consumer engagement.
The team needed a secure, compliant way to scale precision-health reporting.

Built a serverless data pipeline on Google Cloud with Cloud Storage as an infinite data lake.
Used BigQuery as a time-series engine and BigQuery ML to detect non-linear correlations in longitudinal datasets.
Used Gemini Enterprise Agent Platform and Gemini to generate human-readable biomarker narratives and align findings with PubMed.
Delivered reports via Cloud Run and Firebase with secure-by-design IAM and de-identification patterns.

Technologies

BigQuery BigQuery ML Gemini Enterprise Agent Platform Gemini Cloud Storage Cloud Run Firebase

Reduced speed-to-science for genomics annotation from eight weeks to under four weeks.
Reduced data maintenance costs by 30%.
Reduced bioinformatics run costs from $1,500 to $400 per report.

Architecture

vivaLAB uses a serverless Google Cloud architecture with Cloud Storage as an infinite data lake, BigQuery and BigQuery ML for longitudinal analytics, Gemini Enterprise Agent Platform and Gemini for reasoning and natural-language generation, and Cloud Run plus Firebase for real-time report delivery. The system is secured with IAM, de-identification, and VPC controls.

Implementation partners1

Aviato Consulting

Sources & evidence1

Groundedness: 5/5Type: Customer StoryPublished: May 31, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High

Primary source

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Market context

Healthcare industry insights United States country insights

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