Cloud-based diagnostics workflows are scalable, automated pipelines for processing and analyzing diagnostic data in the cloud. Unlike traditional on-premise systems, they utilize cloud computing resources to handle high-throughput sequencing, image analysis, and other diagnostic processes with greater speed, efficiency, and accessibility. This approach enables labs and healthcare providers to scale operations, reduce infrastructure costs, and accelerate time to results.

Cloud computing enables diagnostic tests to scale efficiently by:

• On-Demand Compute Power – Adjusting resources dynamically based on sample volume.
• Parallel Processing – Running multiple diagnostic workflows simultaneously for faster turnaround times.
• Automated Pipelines – Reducing manual intervention, minimizing errors, and increasing reproducibility.
• Global Accessbility – Enabling remote collaboration and real-time data access. This ensures that diagnostic workflows remain agile and capable of handling increasing demand.

Early-stage diagnostics teams often struggle with:

• Limited Infrastructure – High computational costs for running local analysis pipelines.
• Workflow Bottlenecks – Slow data processing due to manual or single-threaded workflows.
• Data Management Complexity – Handling large sequencing datasets and integrating diverse data types.
• Reproducibility Issues – Ensuring consistent results across different batches and experiments.Cloud-based solutions like Polly address these challenges by automating and optimizing workflow execution.

Shifting diagnostic workflows to the cloud offers several advantages:

• Scalability – Easily handle growing sample volumes without upgrading local hardware.
• Cost-Effectiveness – Pay for only the computational resources used, reducing infrastructure expenses.
• Faster Processing – Leverage high-performance cloud instances to reduce turnaround times.
• Collaboration & Security – Enable real-time data sharing while maintaining compliance with security standards.

Polly facilitates this transition by providing a cloud-native diagnostics platform tailored for high-throughput data processing.

Polly accelerates the scaling of diagnostic workflows by:

• Automating Pipeline Execution – Running workflows efficiently with minimal user intervention.
• Cloud-Native Scalability – Dynamically allocating resources to meet workload demands.
• Seamless Data Integration – Connecting sequencing data, metadata, and results in a structured environment.
• Cost Optimization – Reducing compute expenses through intelligent workload distribution.

This allows diagnostics teams to scale operations effortlessly while maintaining high accuracy and reliability.

Running diagnostic pipelines on Polly offers:

• Scalable Infrastructure – Auto-scaling capabilities for handling high-throughput workloads.
• Workflow Automation – Reduced manual effort through seamless pipeline execution.
• Cloud Agnostic Deployment – Compatibility with AWS, GCP, and Azure.
• Data Security & Compliance – Adherence to HIPAA, GDPR, and other regulatory standards.
• Cost-Effective Processing – Optimized compute usage to reduce overall operational costs.

Polly simplifies diagnostics workflows while ensuring efficiency, speed, and reliability.

Polly optimizes cost efficiency through:

• Autoscaling Compute Resources – Allocating cloud resources dynamically to prevent overuse.
• Optimized Workflow Execution – Running tasks in parallel while minimizing idle time.
• On-Demand Pricing Model – Ensuring teams only pay for the compute power they actually use.
• Preemptive Job Scheduling – Identifying and prioritizing critical workloads to reduce costs.

This enables cost-effective scaling of high-throughput diagnostics without unnecessary expenditure.

Yes, Polly is designed to be cloud-agnostic and integrates seamlessly with major cloud platforms like AWS, Google Cloud (GCP), and Microsoft Azure. It supports:

• Native Compute & Storage Integration – Utilizing cloud-based virtual machines and object storage.
• Scalable Cloud Workflows – Deploying diagnostic pipelines with minimal configuration.
• Secure Data Transfer – Ensuring encrypted and compliant data movement across cloud environments.

This flexibility allows teams to choose the cloud provider that best suits their needs.

Polly provides a robust, cloud-native infrastructure that includes:

• High-Performance Compute Clusters – Optimized for parallel execution of bioinformatics workflows.
• Secure Storage Solutions – Encrypted cloud storage for managing large biomedical datasets.
• Automated Job Scheduling – Ensuring efficient and timely processing of diagnostic samples.
• Monitoring & Logging Tools – Real-time tracking of workflow execution and resource utilization.

This infrastructure ensures consistent and reproducible diagnostic results at scale.

Polly’s autoscaling architecture dynamically adjusts compute resources to handle sudden increases in diagnostic sample volume. It:

• Allocates Additional Cloud Resources – Scaling up compute instances in real time.
• Parallelizes Workflow Execution – Running multiple jobs simultaneously for faster results.
• Prioritizes Critical Workloads – Ensuring urgent samples are processed first.
• Minimizes Processing Bottlenecks – Automatically distributing workloads to prevent slowdowns.

This ensures that diagnostics teams can maintain fast turnaround times even during peak demand.

Yes, Polly supports seamless migration of Snakemake and Nextflow workflows. It offers:

• Containerized Workflow Execution – Running Snakemake and Nextflow pipelines within Polly’s environment.
• Preconfigured Compute Resources – Optimizing workflows for cloud execution without major modifications.
• Integration with Cloud Storage – Ensuring smooth data transfer between on-prem and cloud environments.

Teams can easily migrate their existing workflows without needing extensive reconfiguration.

Polly streamlines sample ingestion by automating data import—directly integrating with sequencing platforms and cloud storage—while standardizing metadata for consistency across incoming datasets. It automatically triggers analysis workflows once new data is available and tracks sample progress in real time, reducing manual effort and accelerating diagnostic result generation.

Polly ensures high reliability in diagnostic workflows by:

• Automated Error Detection – Identifying and flagging failed tasks in real time.
• Resumable Job Execution – Restarting workflows from the last successful step to prevent data loss.
• Built-in Logging & Monitoring – Providing detailed error reports for rapid troubleshooting.
• Version Control & Reproducibility – Ensuring consistent pipeline execution across runs.

These safeguards minimize workflow disruptions and enhance diagnostics reliability.

Currently, Polly is optimized for cloud-based deployment to maximize scalability and efficiency. However, for teams with strict data residency or regulatory requirements, custom on-premise deployments may be available upon request. Polly’s flexible architecture allows integration with private cloud environments or hybrid infrastructure solutions.