The decisions made early determine what breaks later.
Complex lab diagnostic development programs fail in predictable ways: field failures, costly rework, and launches that slip. I work with cross-functional teams to get architecture, integration, and data design right before those failure points get built in. Each engagement starts where your program needs it most.
Find your entry point- what's keeping you up at night?
The same decisions keep coming back around.
Decisions get reopened, inherited features stay in scope, and MVP boundaries keep expanding without a clear basis for what actually matters at launch.
The product is taking shape before the workflow is.
Design decisions are locking in across assay, automation, consumables, software, and operator interactions before the full lab workflow has been defined.
The components work. The handoffs don't.
Hardware, software, automation, and data systems function independently, but manual workarounds and integration gaps appear when they have to operate together.
The data exists. You can't reconstruct what happened.
Sample, reagent, and result data live across disconnected systems, and the gaps only become obvious during investigations, audits, scaling, or customer escalations.
- When should a diagnostics or life sciences team bring in a systems architect?
- Before architecture decisions get locked and before validation, while changes are still low-cost. Other common triggers: teams misaligned across assay, automation, software, and operations; scaling that introduces variability; or a workflow that works in theory but fails in practice.
- What is fractional systems and workflow architecture consulting?
- It is senior architecture leadership on a part-time or project basis. Instead of hiring a full-time architect, a program gets end-to-end workflow, integration, and data design expertise for the phase where it matters most, working alongside the existing team.
- Do you help with lab automation, LIMS, and instrument integration?
- Yes. Integration work covers how liquid handlers, instruments, LIMS, and data systems connect, where handoffs fail, and what error handling and traceability are needed so the integrated workflow performs reliably at production scale.
- What does an engagement typically deliver?
- Depending on the entry point: a pressure-tested MVP scope and decision framework, an end-to-end system architecture, an integration risk assessment with prioritized fixes, or a traceability and data architecture that is audit-ready and usable for analytics and AI.