Data & integration
Data foundations and integration for AI
Connect governed operational and analytical data to AI workflows through reliable ELT, APIs, semantic models, and ownership controls.
Operating problem
AI applications inherit the quality, access, and ownership problems of their data. Reliable agents and analytics require deliberate contracts between source systems and consuming workflows.
Useful first outcomes
- Trusted data products
- Observable pipelines
- Clear source ownership
- Reusable integration services
Delivery pattern
- 1Prioritize the consuming AI use case
- 2Profile sources and define data contracts
- 3Build incremental pipelines and service APIs
- 4Monitor freshness, quality, lineage, and cost
Controls built into scope
- Purpose-limited data access
- Schema and quality checks
- Lineage from source to answer
- Failure handling before downstream automation
Services that support this use case
ELT & Data Integration
Connect SaaS, databases, files, and APIs through reliable batch, event, CDC, ETL, or ELT pipelines with testing, lineage, and recoverability.
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Explore serviceCloud & Data Architecture Review
Review cloud platforms, data pipelines, quality, security, cost drivers, and operating risks, then define a practical modernization roadmap.
Explore service