Global Life Sciences & Healthcare Platform

The Challenge

A global life sciences and healthcare platform faced three distinct but interconnected data challenges. Regulatory compliance auditing required analysts to manually cross-examine internal documentation against vendor-supplied regulatory updates — a slow, inconsistent, and error-prone process that created compliance risk at scale. Genomics research workloads involved multi-terabyte sequencing datasets and clinical records that exceeded what any single-node processing system could handle, with variant calling and genotype-phenotype correlation analyses requiring distributed batch compute at a scale the existing infrastructure could not support. And real-time data ingestion from hardware sensors and medical monitoring devices needed a low-latency streaming architecture capable of continuous processing without data loss — a requirement that ruled out batch-oriented ETL approaches entirely. Each challenge required a fundamentally different engineering approach, and all three had to operate within a unified cloud infrastructure on AWS.

Our Solution

GYSP architected end-to-end LLMOps pipelines using LangChain and LlamaIndex, deploying multi-agent systems that autonomously cross-examine internal documentation against vendor regulatory updates, surface discrepancies, and generate structured audit outputs, replacing manual compliance review with an automated agent-driven workflow. A generative chat interface built on Streamlit with GPT-3.5 Turbo integration gave analysts the ability to query regulatory findings conversationally, with live model response streaming for immediate access to audit intelligence. For the genomics workload, Apache Spark clusters were deployed to run distributed batch processing across multi-terabyte genomic sequencing tracks and clinical datasets, executing optimised variant calling pipelines and genotype-phenotype correlation analyses at the scale the research required. Real-time ingestion from hardware sensors and medical monitoring devices was handled by Apache Flink clusters, delivering continuous low-latency stream processing directly from live device feeds. The entire inference layer was hosted on Amazon SageMaker with auto-scaled endpoints ensuring model availability under variable demand, with AWS Glue managing complex serverless ETL transformations across the data integration layer.

Key Deliverables

• End-to-end LLMOps pipelines with LangChain and LlamaIndex orchestrating multi-agent systems for automated regulatory document cross-examination
• Multi-agent audit workflow autonomously comparing internal documentation against vendor regulatory updates and generating structured compliance outputs
• Generative chat interface on Streamlit with GPT-3.5 Turbo streaming live model responses for conversational access to audit intelligence
• Apache Spark distributed batch processing across multi-terabyte genomic sequencing and clinical datasets for variant calling and genotype-phenotype analysis
• Apache Flink clusters delivering real-time low-latency stream ingestion from live hardware sensors and medical monitoring devices
• Amazon SageMaker auto-scaled model endpoints with AWS Glue serverless ETL managing the cloud data integration layer
• Active engagement running continuously since April 2022 across LLMOps, genomics, and real-time streaming workloads

Services Delivered

• AI/ML Development
• Data Engineering
• Cloud & DevOps Engineering

Tech Stack

Frequently Asked Questions