01
A foundation, not a dashboard factory
We model data so many products can use it: analytics, operations and AI retrieval.
Data & Intelligence
Turn Data into Business Intelligence
Pipelines, warehouses, lakes and analytics platforms that make data trustworthy enough for decisions — and ready enough for AI.
The business problem
Where this usually breaks
Reports disagree, pipelines fail quietly, and AI projects stall because the underlying data is fragmented, undocumented or unsafe to use.
operations · overview
Orders in flight
Exceptions
SLA health
Throughput
Queue
Conceptual interface — illustrative of the pattern, not a client system.
Capabilities
What we build here
- 01Data architecture
- 02ETL / ELT pipelines
- 03Data warehouses
- 04Data lakes
- 05Real-time analytics
- 06Business intelligence
- 07Data modernization
- 08AI-ready data platforms
How Zynavia helps
Engineering, not just advice
02
Quality you can operate
Lineage, monitoring and clear ownership so pipelines do not become another legacy system.
Architecture / approach
How the work runs
- 01
Start from decisions
We identify the questions, products and AI use cases the platform must serve.
- 02
Build the backbone
Ingestion, modeling, storage and access patterns are engineered as a platform.
- 03
Activate
BI, APIs and AI retrieval sit on the same trusted data rather than competing copies.
Technologies
Chosen for the workload
Tools are selected against the problem, the existing estate and the team that will operate the system.
SQL ServerPostgreSQLDatabricksSnowflakeBigQueryAzure Data FactoryPower BIPython
FAQ
Common questions
Yes. Chunking, permissions, freshness and source-of-truth design are part of making data usable for AI systems.
Turn Data into Business Intelligence
Pipelines, warehouses, lakes and analytics platforms that make data trustworthy enough for decisions — and ready enough for AI.