Data Infrastructure Engineering Solutions
Three service tiers designed to address different stages of AI infrastructure development — from initial assessment to platform implementation.
Back to HomeOur Service Methodology
Each engagement follows a structured process designed to deliver infrastructure that matches your operational requirements and can be maintained by your internal team after handover.
Discovery
Analysis of current infrastructure, workload characteristics, and technical requirements
Design
Architecture specification, technology selection, and implementation planning
Implementation
Infrastructure deployment, testing, monitoring setup, and performance validation
Handover
Documentation delivery, knowledge transfer, and operational training
Service Offerings
Data Infrastructure Health Check
A technical audit of your existing data infrastructure covering storage architecture, ingestion pipelines, transformation workflows, and delivery mechanisms. The assessment evaluates scalability, reliability, latency, and readiness for AI workloads.
What's Included
- Comprehensive infrastructure inventory and architecture mapping
- Performance benchmarking and bottleneck identification
- Data governance and quality assessment
- Technical findings report with prioritised recommendations
- Improvement roadmap aligned with AI workload requirements
Custom AI Data Pipeline Development
Design and implementation of a purpose-built data pipeline optimised for a specific AI use case — feature engineering for ML models, real-time event streaming, or batch processing for training data preparation.
Deliverables
- Architecture design tailored to your AI use case
- Technology stack selection and justification
- Pipeline implementation with version-controlled code
- Integration testing and performance validation
- Monitoring instrumentation and alerting configuration
- Operational runbook and team training
Enterprise AI Infrastructure Platform
A comprehensive engagement to establish a scalable, production-grade data infrastructure supporting multiple AI workloads. The platform includes data lake or lakehouse architecture, orchestrated pipelines, feature store, model serving infrastructure, and governance layer.
Platform Components
- Data lake or lakehouse architecture implementation
- Orchestrated ingestion and transformation pipelines
- Feature store for ML model training and serving
- Model serving infrastructure with versioning
- Comprehensive monitoring, alerting, and observability
- Data governance layer with lineage and access controls
- 6 months post-implementation support with monthly reviews
Service Comparison
| Capability | Health Check | Custom Pipeline | Enterprise Platform |
|---|---|---|---|
| Infrastructure Assessment | |||
| Technical Recommendations | |||
| Custom Pipeline Development | |||
| Feature Store Implementation | |||
| Model Serving Infrastructure | |||
| Data Governance Layer | |||
| Monitoring & Observability | |||
| Operational Documentation | |||
| Knowledge Transfer | |||
| Ongoing Support Period | 6 Months | ||
| Investment (SGD) | 330 | 910 | 1,990 |
Health Check is for you if:
- • Need clarity on current infrastructure state
- • Planning AI initiatives and want readiness assessment
- • Looking for technical validation before larger investments
Custom Pipeline is for you if:
- • Have a specific AI use case requiring data pipeline
- • Need real-time or batch processing for ML models
- • Want to augment existing infrastructure with new capabilities
Enterprise Platform is for you if:
- • Building long-term AI capability across organisation
- • Supporting multiple AI workloads and ML models
- • Need comprehensive infrastructure foundation
Technical Standards Across All Services
Security Architecture
Data encryption, role-based access controls, audit logging, and secure secrets management integrated from initial design phase.
Infrastructure as Code
All infrastructure defined in version-controlled code enabling reproducible deployments and systematic updates.
Observability Design
Comprehensive monitoring, alerting, and logging tracking pipeline health, data quality, and system performance.
Data Governance
Lineage tracking, schema versioning, data cataloging, and quality validation built into processing workflows.
Scalability Engineering
Systems architected for horizontal scaling with careful partitioning strategies and resource management.
Documentation Standards
Technical documentation, architectural decision records, operational runbooks, and troubleshooting guides included.
Ready to Select the Right Service for Your Needs?
Schedule a consultation to discuss your AI infrastructure requirements and determine which service tier aligns with your objectives.
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