What challenges did Giles AI face before AI implementation?

Manual literature research for clinical trials and regulatory submissions is time-consuming, error-prone, and compliance-heavy. Existing cloud provider fragmentation hindered healthcare compliance and slowed platform development.

What was the impact of the AI solution?

Achieved 85% reduction in clinical research time and 98% concordance between AI and human researchers. Improved trust by designing the system to admit missing data rather than hallucinate. Enabled plans to evolve the assistant towards participating actively in research meetings and medical decision-making using advanced multimodal Gemini capabilities.

Source

GCP

Giles AI Accelerates Clinical Research with Google Cloud AI Platform

Use case typeTherapeutics researchUpdated Jun 13, 2026

Giles AI leverages Google Cloud's unified AI ecosystem to accelerate clinical research and improve compliance. The platform uses Document AI to parse unstructured medical literature and Gemini for complex reasoning to ensure high accuracy and trustworthiness. The infrastructure includes Google Kubernetes Engine and Cloud Run to provide low latency, scalable real-time features. Model Garden on Vertex AI enables specialized model deployment for different healthcare use cases.

Organization: Giles AI
Industry: Healthcare
Location: United Kingdom
Published: May 2026

Reported outcomes

−98%

timeTime & speed

−85%time

Strategic outcomes

Speed & agilityAccelerated clinical research workflowsRisk & complianceImproved compliance for healthcare workflowsNew product / capabilityEnabled advanced medical text parsing and reasoningScale & capacityBuilt scalable real-time AI infrastructure

Catalog median for time & speed deployments: −60% across 727 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 2 of 2

1Clinical Research Acceleration
2Medical AI Assistant

Manual literature research for clinical trials and regulatory submissions is time-consuming, error-prone, and compliance-heavy.
Existing cloud provider fragmentation hindered healthcare compliance and slowed platform development.

Migrated to Google Cloud for a unified secure AI environment with healthcare-tailored compliance.
Used Document AI combined with Gemini models for accurate parsing and reasoning of medical texts.
Built scalable infrastructure using Google Kubernetes Engine and Cloud Run for real-time responsiveness.
Utilized Model Garden on Vertex AI for flexible deployment of specialized AI models for healthcare.
Partnered with Insight for infrastructure landing zone and migration support.

Technologies

Document AI Gemini Google Kubernetes Engine Cloud Run Model Garden on Vertex AI

Achieved 85% reduction in clinical research time and 98% concordance between AI and human researchers.
Improved trust by designing the system to admit missing data rather than hallucinate.
Enabled plans to evolve the assistant towards participating actively in research meetings and medical decision-making using advanced multimodal Gemini capabilities.

Implementation partners2

Insight SADA

Sources & evidence1

Groundedness: 5/5

Primary source

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Customer evidence