The problem
Your data environment has grown through years of projects, acquisitions and reporting needs. You suspect it has become too complex, but you do not have a reliable view of what should stay, what should be modernized, which platforms duplicate each other, and whether it can support analytics and AI in the years ahead.
This usually surfaces during a modernization, cloud migration, acquisition, period of rapid growth, AI initiative or change of leadership.
What we look at
We examine the environment as one system, not as a list of separate technologies:
- source systems and ingestion
- integration and data movement
- storage and transformation
- analytics and reporting
- ownership and boundaries
- governance and security
- data quality and lineage
- observability
- deployment practices
- AI readiness
How AI speeds it up
Agents inventory architecture documents, analyze repositories and configuration, catalog dependencies and compare what is documented with what is actually running. Our architects interpret the evidence, find the structural problems, understand your organizational constraints and set priorities.
What you receive
- Current-state architecture
- Capability map
- Risk and technical-debt assessment
- Ownership and boundary analysis
- AI-readiness assessment
- Priority findings
- Target-state recommendations
- 6–18 month architecture roadmap
- Executive decision summary
“We understand our current architecture, its major risks, what needs to change, and where we should invest next.”