3 projects

Our work

Systems running in production, written up by the engineers who built them. Every one can be read two ways: plain English if you want to know what it does, full technical detail if you want to know how. Looking for how we would approach a sector? Those are the case studies.

Project 02 · Data Engineering

Paginated OData Ingestion: SAP → Azure SQL

SAP's OData service will not hand over a full result set, so this pipeline pages through the daily delta a batch at a time and stops when the source runs dry — no fixed iteration count, no silent truncation.

  • Azure Data Factory
  • ADLS Gen2
  • Azure SQL Database
  • Azure Key Vault
  • Azure DevOps CI/CD
  • Medallion Architecture
  • SAP OData
  • OData Pagination

How it flows

  1. Source02
  2. Control Plane03
  3. Ingestion04
  4. Medallion Layers03
  5. Serving02

Plain English or full technical detail

Project 03 · Data Engineering

Churn Prediction on Databricks: Governed ML with Unity Catalog

A governed end-to-end ML architecture on Databricks: point-in-time features in Unity Catalog, Champion/Challenger deployment by alias, and drift monitoring that triggers its own retraining while promotion stays a human decision.

  • Databricks
  • Unity Catalog
  • Delta Lake
  • MLflow 3
  • Feature Store
  • Model Serving
  • Lakehouse Monitoring
  • Asset Bundles
  • Azure DevOps

How it flows

  1. Data Foundation02
  2. Feature Engineering03
  3. Model Development03
  4. Governance & Registry03
  5. Serving & Action03

Plain English or full technical detail

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Tell us what you are trying to build

Send the problem rather than a spec. We will tell you what it takes, who would work on it, and whether we are the right people for it.

Vijeesh TP

Vijeesh TP

Founder