Lakehouse, semantic layer and analytics copilot
Raw data becomes governed metrics, then answers in plain language.
Use it when
When dashboards disagree, analysts are a bottleneck, or leadership wants to ask questions of the data directly without breaking the definitions.
The parts, top to bottom
across every level
Hover or tap any part to see what it does. The light shows the order a request moves through.
What happens, in order
- 1Ingest batch & streams
- 2Bronze → silver → gold tables, partitioned by time / tenant
- 3Quality tests & lineage
- 4Semantic layer defines metrics
- 5Text-to-SQL agent with guardrails
- 6Dashboards, alerts, exports
What we typically build it with
Trade-offs
The copilot is the easy part. In Spider 2.0's original result the reference agent solved about 21% of real enterprise text-to-SQL tasks, while dbt's own test put accuracy through a governed semantic layer at 83% against a 16.7% raw-SQL baseline. Partition by time and tenant, cluster on common filters, and avoid small files so every query scans less.
Problems this architecture solves
Generic problem statements with the flow and the outcomes the industry has documented.
Every question needs an analyst
Leaders wait days for a report, dashboards disagree with each other, and the data team is a queue. Raw data sits in the warehouse without becoming a decision.
How the system works
- Raw sources
- Lakehouse with partitions & quality checks
- Semantic layer of metrics
- Text-to-SQL agent
- Dashboards & alerts
Outcome: In dbt's own test, questions answered through a governed semantic layer reached 83% accuracy, against a 16.7% raw-SQL baseline in the data.world benchmark it replicated; vendors report time-to-insight falling by over 99% with 62% adoption within a year. dbt: the semantic layer as the data interface for LLMs
ArchitectureOther data patterns
Document intelligence pipeline
Unstructured files in, validated records out, humans only on the uncertain ones.
Use it when: Invoices, KYC, claims, contracts, forms and emails that must become structured data in a system of record with an audit trail.
Flow
- 1Ingest from email, scan, upload
- 2Classify document type
- 3Extract with a typed schema
- 4Validate against rules & other docs
- 5Confidence routing: auto or review
Let's build intelligent systems that drive growth
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