Platform Design & Blueprint
Design a production-ready, AI-first platform tailored to your tech stack, data landscape, and compliance needs.
Objective
Design a production-ready, AI-first platform tailored to your tech stack, data landscape, and compliance needs.
What's Included
Cloud & Platform Architecture
Modular reference architecture (multi-cloud, on-prem, hybrid)
Data & AI Workload Design
Patterns for model training, inference, retraining, and observability
MLOps Blueprint
Toolchain selection (e.g., Kubeflow, MLflow, ClearML), CI/CD for ML
Governance Framework
AI lifecycle governance, FinOps, security policies, RBAC models
What You Get
Technical documentation and architecture diagrams
Complete technical specifications with visual architecture blueprints.
Blueprint with recommended tech stack
Detailed technology selection and modular component design.
Implementation-ready governance templates
Policy-as-code, access controls, and compliance frameworks.
Integration mapping
For your existing systems (CI/CD, CMDB, IAM).
Benefits
Accelerate Time to Production
Fast-track AI projects with proven architecture patterns
Future-Proof Architecture
Built for AI Act & GDPR compliance from the ground up
Avoid Vendor Lock-in
Open-source and modular design for maximum flexibility
Technology Stack & Products
RHEL AI
Red Hat Enterprise Linux optimized for AI workloads with GPU acceleration and ML frameworks
Red Hat OpenShift AI
Enterprise AI platform for model development, deployment, and lifecycle management
NVIDIA Triton
High-performance inference server for deploying AI models from multiple frameworks
Run.ai
AI workload orchestration and resource management for Kubernetes environments
Cloud Deployment Examples
AWS AI Platform
- EKS for Kubernetes orchestration
- SageMaker for model training
- S3 for data lake storage
- CloudWatch for monitoring
Azure AI Platform
- AKS for container orchestration
- Azure ML for experiment tracking
- Blob Storage for data management
- Azure Monitor for observability
Google Cloud AI Platform
- GKE for Kubernetes clusters
- Vertex AI for unified ML platform
- BigQuery for data analytics
- Cloud Operations for monitoring
Hybrid Cloud Setup
- On-premises Kubernetes + cloud burst
- Multi-cloud data replication
- Unified governance across environments
- Consistent security policies
Real-World Implementation Examples
Financial Services: Credit Risk Modeling
Challenge
Legacy credit scoring system couldn't handle real-time risk assessment for millions of transactions.
Solution
Deployed ML pipeline with automated model retraining, reducing false positives by 40% and processing time by 60%.
Healthcare: Medical Image Analysis
Challenge
Radiologists overwhelmed by increasing imaging volumes with inconsistent analysis quality.
Solution
Implemented AI-assisted diagnosis platform with GPU acceleration, improving detection accuracy by 35% and reducing analysis time by 50%.
Retail: Demand Forecasting
Challenge
Inaccurate inventory forecasting leading to stockouts and excess inventory costs.
Solution
Built automated forecasting system processing 10M+ data points daily, improving forecast accuracy by 45% and reducing inventory costs by 25%.
Proven Framework, Tailored to You
We've implemented this blueprint in financial services and digital-native scaleups. You get a proven framework that balances speed, compliance, and cost control.
Design Your PlatformFrequently Asked Questions
Do we need to rebuild everything from scratch?
Rarely. Good platform design builds on your existing investments. We evaluate your current tools, infrastructure, and team skills, then design an architecture that extends what works and replaces what doesn't. Most designs integrate with existing CI/CD, monitoring, and cloud accounts.
How do you handle multi-cloud requirements?
We design abstraction layers that work across cloud providers — using Kubernetes, Terraform, and infrastructure-as-code patterns that aren't vendor-locked. The platform provides a consistent developer experience regardless of the underlying cloud provider.
What's the difference between design and implementation?
Design produces the blueprint: architecture decisions, technology selections, security model, cost projections, and phased delivery plan. Implementation is the actual build. Some clients use our design with their own teams; others continue with us through implementation.