Any source in. Governed at every hop. Real time, in and out, safely.
This is the reference architecture behind the platforms we build: built-in and custom connectors feed a bronze/silver/gold medallion pipeline, while an AI-first governance layer watches every hop for schema drift, corrects what it safely can, and keeps a full lineage trail, continuously, not as a quarterly audit exercise.
One diagram, source to consumer.
Data plane on the bottom, AI governance control plane on top, watching every hop in between.
Built-in connectors cover the common sources out of the box; a custom connector slot, speaking MCP where an agent needs to reach the pipeline directly, takes anything else without new pipeline code. Data moves in and out in real time via change data capture (CDC) and event streaming, not nightly batch, inside a zone encrypted in transit and at rest the whole way through, from bronze (raw, as landed) to silver (cleansed and conformed) to gold (curated, business-ready). An AI-first governance layer sits above all of it: continuous monitoring, full source-to-gold lineage, schema drift detection the moment it happens, and auto-correction for what it can safely fix on its own.
Turn it on at your pace, not ours.
Every stage below stands on its own. Stop wherever it fits, start the next one when you're ready.
Visibility
Monitoring and tracking switched on first, no changes to your existing pipelines.
Lineage
Full source-to-gold lineage mapped across every hop, so every field is traceable.
Drift detection
AI watches every schema in the pipeline and flags drift the moment it happens.
Auto-correction
AI resolves the drift it can safely resolve, and routes the rest to a human.
Governed autonomy
Full AI-first governance running continuously, humans in the loop only where it matters.
Built on tools we run in production.
Kept at the pattern level here, on purpose; implementation specifics stay inside each engagement.
The same lake and lakehouse icon repeats across Azure, AWS, and Google Cloud on purpose: it's one architectural pattern, whichever cloud it lands on. Production delivery today is Azure and AWS; Google Cloud is the same medallion pattern mapped onto its equivalents when an engagement calls for it.
Beyond what's already been built, here's the broader SaaS map by category and cloud, useful for scoping an engagement or evaluating what an org already has before we design around it. This is landscape knowledge for planning purposes, not a delivery roster; the production track record stays the Azure and AWS grid above.
| Category | Azure | AWS | Google Cloud | Cross-cloud / SaaS |
|---|---|---|---|---|
| Data warehousing | Synapse Analytics, Azure SQL DW, Microsoft Fabric | Redshift | BigQuery | Snowflake |
| ETL | Data Factory, SSIS | Glue | Dataflow, Cloud Data Fusion | Informatica, Talend |
| ELT | Data Factory (ELT mode) | Glue, Redshift Spectrum | BigQuery Data Transfer | Fivetran, Airbyte, Matillion |
| Transformation | Databricks, Synapse Spark Pools | Glue Studio, EMR | Dataproc | dbt, Delta Lake, Apache Iceberg |
| Data integration | Event Hub, Logic Apps, API Mgmt | AppFlow, EventBridge, Kinesis | Pub/Sub, Apigee | Confluent (Kafka), MuleSoft, Boomi |
| Data governance | Microsoft Purview | Glue Data Catalog, Lake Formation | Dataplex | Collibra, Alation |
| Machine learning | Azure Machine Learning | SageMaker | Vertex AI | Databricks ML, MLflow |
| AI / GenAI | AI Foundry, Azure OpenAI | Bedrock | Vertex AI (Gemini) | Claude, OpenAI API, LangChain |