Proof, in production.

Nightly batch pipelines could no longer keep up with the volume of network telemetry. Fault signals surfaced hours late, and reprocessing a failed run pushed insight another full day out.

1. 1Introduced change-data-capture and streaming ingestion to replace brittle nightly extracts.
2. 2Rebuilt transformations as idempotent, replayable jobs orchestrated in Airflow.
3. 3Added data-quality monitors and SLAs so on-call sees breakage before the business does.

A streaming backbone that sustains millions of events per second, with self-healing reprocessing and quality gates guarding every stage.

Ordering was driven by branch intuition. Fast movers ran dry while slow stock aged on shelves, and no one could see demand shifting until it had already cost a sale.

1. 1Built per-SKU, per-branch demand models blending seasonality, promotions and lead times.
2. 2Wrapped forecasts in a replenishment view managers trust and can override.
3. 3Set up drift and accuracy monitoring so the models stay honest as demand moves.

Forecast-driven replenishment that keeps fast movers in stock and stops capital aging on the shelf — with humans firmly in the loop.

A growing marketplace was drowning in user-generated listings. Manual review couldn't scale, harmful content slipped through, and reviewers burned out on obvious spam.

1. 1Combined text classification with vision detection to auto-clear and auto-block the clear cases.
2. 2Routed only ambiguous items to reviewers, ranked by risk and confidence.
3. 3Instrumented the whole loop for precision, recall and appeal rates — tuned continuously.

A layered moderation pipeline where models handle the volume and humans handle the judgement calls, with safety metrics visible end to end.

Field reps captured orders and store audits on paper, then re-keyed them days later. The commercial team was always looking at last week's reality, and errors crept in at every hand-off.

1. 1Built a mobile Power App with offline capture for orders, audits and photos.
2. 2Backed it with Dataverse and automated the sync to the ordering system.
3. 3Surfaced route performance in Power BI for managers the moment data lands.

A low-code app the field team adopted in days, feeding clean data straight into a live commercial dashboard — no more clipboards, no more re-keying.

The on-prem warehouse had run out of headroom. Month-end contention slowed every report, and scaling meant buying hardware months ahead of demand.

1. 1Profiled workloads and re-platformed pipelines to serverless, pay-per-use compute.
2. 2Migrated historical data with reconciliation checks at every step — zero silent loss.
3. 3Automated infrastructure as code so environments are reproducible and auditable.

A serverless cloud platform that scales with demand instead of ahead of it, cutting run cost while removing the month-end bottleneck for good.