Selected work / eight systems
Two private flagship systems and six public, anonymized reference implementations. Each case study explains the business problem, system boundaries, operational design, and what can actually be inspected.
Discuss a projectPrivate product · Analytics infrastructure
Private systemA private B2B analytics platform that turns business-facing definitions into the ingestion, transformation, caching, and execution required to answer them.
Private system · Engineering operations
Private systemA control plane for moving software work from requirement to independently verified handoff across AI coding providers without losing ownership, evidence, or state.
Public reference · Data platform
GitHub ↗A queue-first snapshot and change-data-capture service that preserves event order, durable resume state, complete SCD2 history, and a current PostgreSQL view.
Public reference · Data platform
GitHub ↗A configurable ingestion service for snapshots, live change streams, scheduled collections, and targeted repairs into partitioned object storage.
Public reference · Serverless backend
GitHub ↗An event-driven AWS workflow that detects when sponsored creators go live, starts session monitoring, and records audience, chat, transcript, and mention telemetry.
Public reference · Applied AI
GitHub ↗A registry-driven workflow that screens source material, generates structured content, routes invalid output, and prepares valid results for human review.
Public reference · Generative AI
GitHub ↗A GPU-oriented workflow for adapting SDXL to an existing visual language, generating traceable variations, filtering failures, and handing candidates to designers.
Public reference · ML and product intelligence
GitHub ↗A Databricks and PySpark workflow that converts high-volume community chat into sentiment trends and privacy-conscious aggregate research personas.
The public repositories are a few selected, anonymized reference implementations based on systems I designed and built. They use synthetic data and deliberately omit client credentials, proprietary configuration, original datasets, private model artifacts, and confidential business logic.
Have a similar problem?