> describe services/data-platforms-infrastructure
02 -- Data Platforms & Infrastructure
Foundations for Production AI
We design and build the data infrastructure that production AI systems require. This includes ingestion pipelines, enrichment workflows, vector stores, and orchestration layers that can scale.
Our approach treats data as a product, not a byproduct. We build systems that are observable, maintainable, and designed for continuous evolution.
> ls ./services/data-platforms/capabilities/
  • Data Architecture -- Design scalable, cost-effective data platforms for AI workloads
  • Ingestion Pipelines -- Build reliable data pipelines from multiple sources
  • Data Enrichment -- Augment internal data with external signals and intelligence
  • Vector Stores -- Implement and optimise vector databases for RAG and semantic search
  • Orchestration -- Workflow automation and pipeline orchestration at scale
  • Observability -- Monitoring, logging, and data quality frameworks
> cat ./services/data-platforms/use-cases.md
Typical Engagements:
  • Building unified customer data platforms for AI personalisation
  • Implementing vector search infrastructure for knowledge systems
  • Designing real-time data pipelines for operational AI
  • Data platform modernisation and cloud migration
  • Building data mesh architectures for large organisations
> cd .. -- Return to services menu
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