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Life Sciences

AI that accelerates discovery and survives FDA scrutiny

You get AI agents for clinical trial operations, research intelligence, and regulatory documentation — built to meet the FDA's 2025 AI guidance framework, running on your cloud with full audit trails.

or

21 CFR Part 11 compliant. Built on your AWS, Azure or GCP tenant. IRB and FDA-ready.

Life sciences laboratory research
30 days

Proof of concept

12–18 weeks

To measurable ROI

Your cloud

AWS, Azure or GCP

The FDA issued its first AI guidance in January 2025. Most organisations are not ready.

AI that influences GxP decisions — manufacturing, labelling, safety, QC, batch release, or clinical data interpretation — is now subject to device-level controls. The FDA has already issued warning letters for misclassified AI products.

We build AI that meets the credibility assessment framework the FDA requires: defined context of use, risk-assessed models, verified and validated outputs, and documented results — before we write a line of production code.

FDA 2025

first AI guidance for drug and biological products issued

21 CFR 211

validation standard for AI in GMP manufacturing

EMA Annex 22

new EU GMP annex bridging AI and pharma standards

IRB + legal

governance frameworks your review boards can sign off

Connected

From research data to regulatory-ready output — in one workflow

We connect your EDC, LIMS, EHR, and study management systems to AI agents that accelerate trial operations, flag protocol deviations, and generate documentation that meets FDA and EMA submission standards — all on your cloud tenant

Trial & Research Data

EDC, LIMS, EHR and study systems

AI Agent
ModelYour choice
HostingYour cloud
Workflow Engine
  • Approval gates
  • Human-in-loop
  • Audit logging
Your Cloud Tenant

AWS, Azure or GCP

Regulatory Output
  • FDA submissions
  • Audit trails
  • 21 CFR Part 11
5 Integrations
4 Connections

What we deliver for life sciences organisations

Life Sciences AI

FDA-ready agents built for regulated research environments

Clinical trial recruitment

Screen patient data and accelerate trial enrolment.

ROI

Recruitment speed40% faster
Protocol matching+30%
Screening accuracy95%+

Process

1

Screen patient data

EHR records and patient registries queried against inclusion/exclusion criteria automatically

2

Match and rank

AI scores patient eligibility and ranks candidates by protocol fit and site proximity

3

Accelerate enrolment

Pre-screened candidate lists delivered to sites with documentation for IRB review

Sources

Originally published: AWS Case Study — Roche; Azure Case Study — Pfizer — reproduced for illustrative purposes

Protocol deviation detection

Flag protocol deviations in real time with full audit trails.

ROI

Deviation flagging speed50% faster
Protocol amendments−35%
Detection coverage100%

Process

1

Monitor trial data

EDC entries, lab results, and visit schedules streamed and compared against protocol requirements

2

Flag deviations

AI detects protocol deviations in real time — missed visits, dosing errors, and eligibility violations

3

Document for review

Deviation records with full decision trails generated for IRB and CRO review

Sources

Originally published: GCP Case Study — Medidata/Dassault; Azure Case Study — Parexel — reproduced for illustrative purposes

Regulatory submission support

Structure and quality-check eCTD submissions automatically.

ROI

eCTD assembly speed45% faster
Review cycle reduction30%
Consistency checksAutomated

Process

1

Structure submission content

Study data, CMC sections, and clinical narratives organised into eCTD format

2

Quality-check documents

AI cross-references data tables, narratives, and regulatory requirements for consistency

3

Prepare for filing

Flagged issues resolved, eCTD package assembled for regulatory affairs final review

Sources

Originally published: AWS Case Study — Veeva; Azure Case Study — IQVIA — reproduced for illustrative purposes

Research intelligence

Search and synthesise your internal research corpus securely.

ROI

Literature review speed60% faster
Experiment analysis40% faster
Knowledge reuse+50%

Process

1

Index research corpus

Internal publications, study reports, and experimental data indexed with access controls preserved

2

Search and synthesise

Natural language queries return relevant findings with cross-study synthesis and citation trails

3

Deliver insights

Summarised research intelligence with source links and data classification labels

Sources

Originally published: GCP Case Study — Elsevier; AWS Case Study — Benchling — reproduced for illustrative purposes

Pharmacovigilance automation

Classify adverse events and draft ICSRs automatically.

ROI

AE processing speed55% faster
ICSR cycle time−40%
Case completeness99%+

Process

1

Ingest AE reports

Adverse event data from trials, spontaneous reports, and literature ingested automatically

2

Classify and prioritise

AI scores signal severity, assesses causality, and ranks cases for medical review

3

Draft ICSRs

Individual case safety reports drafted with human-in-the-loop review before submission

Sources

Originally published: Azure Case Study — IQVIA; AWS Case Study — Oracle Argus — reproduced for illustrative purposes

Lab data analysis

Flag lab anomalies and escalate results for scientific review.

ROI

Result analysis speed35% faster
Anomaly detection+25%
Audit trail100%

Process

1

Process LIMS outputs

Analytical results from instruments ingested via LIMS with full chain-of-custody preserved

2

Detect anomalies

AI flags out-of-range results, trend deviations, and instrument drift requiring investigation

3

Escalate with context

Anomalous results delivered to scientists with historical context and recommended actions

Sources

Originally published: GCP Case Study — Thermo Fisher; Azure Case Study — Waters — reproduced for illustrative purposes

Study reporting and narratives

Generate study reports and patient narratives from trial data.

ROI

CSR generation speed50% faster
Narrative writing time−40%
First-draft quality90%+

Process

1

Extract trial data

Efficacy, safety, and demographic data extracted from EDC and statistical analysis outputs

2

Generate draft reports

AI produces CSR sections, study summaries, and patient narratives from structured data

3

Review and finalise

Medical writers review AI-generated drafts, refine language, and approve for submission

Sources

Originally published: AWS Case Study — Certara; GCP Case Study — Medidata — reproduced for illustrative purposes

Outcomes and biomarker analysis

Identify biomarker patterns and patient subgroups in trial data.

ROI

Biomarker identification3x faster
Genomic analysis speed50% faster
Subgroup discoveryAutomated

Process

1

Integrate multi-omic data

Genomic, proteomic, and clinical data unified for biomarker discovery and subgroup analysis

2

Identify patterns

ML models detect biomarker signatures, patient subgroups, and outcome predictors

3

Deliver explainable outputs

Results presented with feature importance, confidence intervals, and clinician-readable explanations

Sources

Originally published: GCP Case Study — Tempus; AWS Case Study — Foundation Medicine — reproduced for illustrative purposes

21 CFR Part 11 compliance

21 CFR Part 11 compliance built in from day one.

ROI

Validation speed60% faster
Part 11 audit prep−45%
Compliance coverage100%

Process

1

Build compliant architecture

Electronic records, signatures, and audit trails designed into every AI workflow from day one

2

Validate and document

IQ/OQ/PQ executed with evidence packages structured for FDA inspection readiness

3

Maintain and revalidate

Change control and periodic review protocols established for ongoing Part 11 compliance

Sources

Originally published: AWS Case Study — Veeva Vault; GCP Case Study — Benchling — reproduced for illustrative purposes

Emmanuel Frenehard

EVP & Chief Digital Officer, Sanofi

Emmanuel Frenehard

"AWS is helping us accelerate our AI-powered drug discovery and clinical development programmes. By deploying large-scale machine learning on AWS, Sanofi is transforming how we identify, design, and test new therapies — compressing timelines that once took years into months"

Originally published: AWS Customer Stories — Sanofi — reproduced for illustrative purposes

01 / 04

How we work in regulated life sciences environments

We map each AI use case to your regulatory obligations before we write code. We assess model risk using the FDA's credibility assessment framework. We build 21 CFR Part 11-compliant audit trails into every workflow. And we run everything on your cloud tenant — your data never leaves your environment.

Every agent we deploy can be explained to your IRB, your legal team, your data privacy officers, and your regulators — because we document governance as we build, not after the fact.

01

Regulatory mapping

We identify your use case's regulatory classification and map it to FDA, EMA, or ICH obligations before any build begins.

02

30-day proof of concept

A production-ready AI agent on your cloud tenant, integrated with your existing data systems and compliant from day one.

03

Governance by design

Audit trails, explainable outputs, and human-in-the-loop controls built to meet FDA credibility assessment requirements.

04

Scale in 12–18 weeks

From one validated workflow to measurable operational ROI — with documentation your regulatory affairs team can submit.

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