DEEP

The problem

Collecting information is only the beginning of a data product. Source material needs to be normalized, interpreted, connected to existing records, and checked before it becomes useful.

Some steps can be automated. Others need a person to investigate an ambiguity or make a decision. A reliable workflow has to accommodate both and retain the context behind the result.

What was built

DEEP is the processing and review system I work on behind this kind of data infrastructure. It organizes work into pipelines, combining automated processing with analyst tasks and quality checks.

The emphasis is on carrying information through a sequence of decisions: receiving source material, extracting and transforming it, matching it to records, reviewing uncertain cases, and preparing it for use downstream.

Keeping the source and the history of those decisions makes the resulting information easier to explain and correct.

My work

I work on the pipeline architecture, processing steps, and interfaces for people reviewing the data. The aim is to make automation and human judgment parts of the same system, with clear transitions between them.

This is infrastructure work: the visible product depends on what happens before a record reaches the screen.