Case Studies
How these engagements actually run
Three representative engagements — the situation we were brought into, the decisions we made, the architecture we built, and what changed as a result.
Engagements
Three patterns we are repeatedly asked for
Each write-up follows the same structure, so the approach and trade-offs can be compared rather than admired.
Retail
01Enterprise data warehouse migration to BigQuery
A national retailer moves off an ageing on-premises appliance, cutting warehouse running costs and turning overnight reporting into same-hour analysis.
- BigQuery
- Cloud Storage
- Dataflow
- Pub/Sub
Manufacturing & Energy
02Predictive maintenance on Vertex AI
A global manufacturer moves from a stalled proof of concept to monitored models in production, catching failures before they stop a line.
- Vertex AI
- BigQuery
- Dataflow
- Pub/Sub
Financial Services
03Document processing automation with generative AI
A financial services provider automates onboarding document handling with grounded extraction, human review and a complete audit trail.
- Vertex AI
- Gemini
- Cloud Storage
- BigQuery
These engagements are anonymized and presented as representative scenarios. They describe the architecture patterns, decisions and trade-offs typical of our work rather than the details of a specific named client. We publish identifiable engagement details only with written client approval.
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