Most AI projects skip the hard part. They start with the model. The model is the easy part. The hard part is getting structured data out of messy clinical and financial systems where formats change without warning and compliance isn’t optional.
Regulated data has its own physics
HL7 v2.x messages arrive malformed. FHIR R4 resources miss required fields. Proprietary EHR exports use undocumented delimiters. Financial feeds switch schemas between versions. I engineer pipelines that parse, validate, and transform all of it into clean, queryable data — regardless of what the source system decides to do today.
Garbage in, expensive garbage out
A model trained on dirty data doesn’t give wrong answers occasionally. It gives wrong answers confidently. In healthcare, that’s a patient safety issue. In finance, that’s a compliance violation. The pipeline is not infrastructure work you do before the real project starts. The pipeline is the project.
What ships
Data ingestion from HL7, FHIR, MLLP, and proprietary formats. Validation against regulatory schemas. Transformation into structures your models and analytics can actually use. Monitored, logged, auditable.