AI & Data Platform Engineering
We build governed data and AI platforms on Azure so data scientists and engineers can experiment quickly—and ship to production with lineage, quality gates, and model lifecycle discipline. Generative AI and RAG are integrated where they add value, not bolted on as demos.
A strong data platform makes AI cheaper to operate: reusable pipelines, clear ownership, and monitoring that catches drift before users do.
Modern data platforms & lakehouse patterns
We design ingestion, transformation, and serving layers that match your domains—batch, streaming, or hybrid—without over-engineering. Medallion or similar patterns are applied pragmatically so bronze/silver/gold (or your naming) reflects how teams actually work.
Analytics, warehousing, and self-service boundaries
Synapse, Fabric, dedicated SQL pools, or lakehouse engines are chosen against workload fit, not hype. We define who can publish datasets, how semantic models are governed, and how cost scales with consumption.
ML platforms, MLOps, and model lifecycle
Azure Machine Learning workspaces, registries, and pipelines support training, experiment tracking, and promotion to production. We design monitoring for data drift, model quality, and inference latency—so models can be retired or retrained on evidence.
LLMs, RAG, and governed API access
RAG architectures connect retrieval, embedding stores, and orchestration with access control and logging suitable for enterprise use. API gateways, token budgets, and content policies reduce the risk of shadow AI spreading unchecked.
Outcomes you can expect
- A single place for trusted datasets with documented lineage and stewards
- Faster time from experiment to production for ML and analytics workloads
- Clear separation between exploration sandboxes and governed production paths
- Operational dashboards for pipeline health, costs, and model performance
- Reduced duplicate data pipelines and ad-hoc copies across teams
Where we add the most value
- Enterprises scaling from spreadsheets and siloed warehouses to a unified platform
- Teams piloting generative AI who need architecture—not only prompt guides
- Organizations under pressure to show ROI on data and AI investments
- Regulated environments needing explainability and access logging for models
Representative technologies
- Azure Data Lake Gen2
- Azure Synapse / Microsoft Fabric
- Azure Machine Learning
- Azure OpenAI Service
- Databricks (when already strategic)
- Event Hubs / Stream Analytics
- Purview (governance & catalog)
What we typically deliver
- Data & AI landing zone and network isolation design
- Lakehouse / warehouse architecture with domain-oriented boundaries
- ML platform and MLOps patterns on Azure ML
- RAG and LLM integration patterns with governance hooks
- Ingestion and transformation patterns (Spark, dbt, or Azure-native)
- Cost and capacity planning for compute and storage tiers
- Documentation for data stewards, engineers, and security reviewers
- Executive narrative linking platform investment to business use cases