Consulting and
Technical Services Pricing

Opting for a replatform approach, we migrated the the database using AWS Database Migration Service (DMS) for a seamless transition of SQL Server databases to Aurora. AWS Elastic Beanstalk replaced .NET applications for auto-scaling and  monitoring. AWS Cognito secured and streamlined authentication. Well-Architected best practices targeted operations and cost optimization.

We devised a refactor strategy, leveraging the AWS SaaS Lens framework to guide best practices in transitioning to a microservices-based, cloud-native SaaS architecture. Key microservices were used AWS Lambda for optimal scalability and efficiency. Application components were containerized, using Kubernetes orchestration EKS. The DB migration to Amazon Aurora was executed with tenant data separation as a priority.

Our DevOps-as-a-Service package offering combines managed AWS and cloud operations support, application support, along with Kubernetes (K8s) cluster administration. It spans a developer toolchain, Landing Zones, Access, and Change Management. 40 hours/month covers dedicated DevOps support, with application-level support, log analytics, observability, cost optimization, and regular system upgrades.

We containerized existing business processes, eliminating the need to re-implement crucial data services. A new self-service tier running on Kubernetes clusters ran provisioning via a pooled multi-tenancy architecture. The effort returned a 10x ROI in months, by enabling sales to local businesses at a much more attractive price point.

We optimized the software development pipeline with Infrastructure as Code (IaC) practices, modularizing provisioning and adding Vault for centralized security management. The newly streamlined release process let legacy data-center customers access the latest Diffusion Cloud features, giving the development team to efficiently roll out new functionalities.

Working with the company's technical leaders we implemented a unified Platform Engineering strategy via GitHub Actions, Argo, Harbor, Helm, and Prometheus. Integrating these tools into a coherent platform  Backstage-based Developer Portal. We facilitated the migration and served as the interim Platform Team.

Our engineers onboarded specialized  tools such as MLFlow and KubeFlow to adapt  the company's processes, then helped the company's engineers learn to use them for governance of dynamic training data for ongoing operations.

We leveraged Python and AI to provide real-time insights from the data, enabling the customer to enable predictive maintenance, anomaly detection, and optimization algorithms.

We assembled a team of application developers, ML engineers, data engineers, and DevOps professionals. They were tasked with integrating dashcam video streams with drivers' smartphones, preprocessing stream data on devices using TensorFlow Lite, and establishing systems for data collection and processing. A data lake set up w/Databricks supported the company's data scientists in model training and productization on AWS.

CloudGeometry leveraged Generative AI, creating custom GAN models trained on diverse datasets of artistic styles and user preferences. Integration of the AI-driven artwork generation tool increased user engagement and retention on the platform, leading to a 30% increase in user interactions and a 20% rise in platform revenue.

We used tools such as KubeFlow to create an MLOps pipeline that enabled customers use of trained models to detect patterns and suggest additional DNA combinations, for better research outcomes using fewer precious resources.