A molecular workflow looks like a straight line from the outside. Laboratorians know different.

For twenty-five years the industry drove down the cost of the assay itself: sequencing chemistry, PCR, dry-lab automation. The assay keeps getting cheaper. What hasn’t kept pace is everything it takes to run it at scale, and that’s where the gain that’s left actually is.

I saw it from every seat I held, not one piece at a time: labs regressing to send-out testing when the unit economics stopped working, implementations stalling for months, in some cases labs closing their doors. The cause usually isn’t the technology itself; it’s an operations problem. E2E sample tracking held together by a spreadsheet, manual data entry between systems, overlapping system boundaries, custom code force-fitting platforms into work they were never designed for, a standing reliance on professional services. All of this drives the cost per test well above what anyone estimated.

Three things drive it:

• Procurement requirements that were never properly scoped. The lab wrote a spec based on the need for one component and didn’t model how that component fits into their E2E workflow, both today and in the future.
• A workflow and total cost of ownership nobody modeled at the process level. High-level estimates leave out the nuance of a fully burdened cost analysis.
• Molecular labs are highly variable and constantly changing, which often results in custom code and shadow systems to fill the gap between system capability and operational needs.

Nobody chose these intentionally. They accumulate quietly and compound until they choke a lab’s ability to grow and erode its profitability.

What was unusual about my path is that I had the whole system in view at once, not as a bench scientist or a software engineer, but as the person who had to make all of it make sense together. The thesis behind HelixWrks came from that complete view, not from any one seat along the way.