Welcome to The Blueprint, a new feature where we highlight how Google Cloud customers are tackling unique and common challenges across industries using the latest AI and cloud technologies. We hope to inspire others looking to innovate in their work.
The challenge:
Giles AI is a London-based startup that helps healthcare and life sciences organizations quickly extract insights from fragmented data, whether that data is available in an online repository (e.g. PubMed, NICE, the FDA etc.), local documents, or internal IP. Users can connect to internal and external data repositories and upload documents and images to the Giles AI platform; this integration allows users to the combined knowledge base for insights more quickly and efficiently, using natural language prompts and an intuitive interface.
From our partners:
As Giles AI grew in popularity, our incumbent cloud provider struggled to cope with complex data flows, new LLMs, and external APIs. Latency increased, slowing the user interface and impacting critical activities. Engineers also required a more agile development environment. Security is also a foundational feature of Giles AI and everything we build — with clinical, medical, and healthcare standards in mind, sensitive data must be protected at every step, both at rest and in transit.
The solution:
Giles AI leveraged Google Cloud’s modular, API-friendly, microservices-based architecture to minimize latency, easily manage complex clinical data flows in real-time, and capitalize on the latest and greatest AI foundation models as they are released.
Backend service orchestration in Google Kubernetes Engine and lightweight microservices in Cloud Run are complemented by specialized workloads in Compute Engine to keep the Giles AI platform available, flexible, and scalable without the heavy management and maintenance demands of legacy infrastructure. Cloud Load Balancing ensures efficiency.
Cloud SQL, Cloud Storage, and Document AI help the Giles AI platform manage structured and unstructured data and extract insights. Under the hood, Vertex AI handles model selection and prompt orchestration. The system is model-agnostic by design, enabling Giles AI to route queries to the most appropriate language model including hundreds available through Model Garden on Vertex AI.
With this highly flexible approach, Giles AI is able to deliver numerous healthcare and life sciences use cases from systematic literature reviews and regulatory reviews to meta-analyses, data extraction, and patient eligibility screening — all with high levels of accuracy and agreement.
To enhance security, we’re leveraging Cloud Armor to defend against Web-based attacks and Security Command Center to keep a close eye on its posture. Google Cloud regional databases help Giles AI localize data at rest — a critical need given healthcare regulations.
The architecture:
The conclusion:
What we love about Vertex AI is that it supports our AI workflow experimentation. In simple terms, this means we can plug any LLM of our choice in and out of our workflow, drawing from the hundreds of models available in the Model Garden on Vertex AI. This provides amazing flexibility and efficiency, which is key to our success.
So far, the results of our migration have been impressive.
One of Giles AI’s early customers achieved an 85% reduction in the time required for clinical research tasks and over 94% response accuracy, with references provided when they wanted to be certain and verify. This customer was so compelled with the results that they went on to make a significant investment into the company and became a strategic partner.
Latency, uptime, and scalability have all improved significantly, even with complex, multi-layered data queries. From an internal perspective, Giles AI has seen an increase in developer velocity, with infrastructure-as-code and managed services reducing engineering overheads.
The Giles AI generative AI assistant interface
Looking to the future, our team at Giles AI is excited for the potential of Google Cloud’s AI foundation models designed for the medical community. These include MedGemma, a family of open-source AI models tailored for medical applications, and TxGemma, a suite of open therapeutic-language models derived from Gemma 2 that help streamline drug discovery and development.
With these powerful tools on the horizon, Giles AI is poised to deliver smarter, more verticalized decision-making across the entire healthcare R&D pipeline. For clients, this means turning complex data into real-world breakthroughs, faster than ever before.
Source: zedreviews.com
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