People treat a molecular workflow as a linear sequence of steps. It isn’t — it’s a complex system of systems, where the bench, the instruments, the software, the data, and the business each run on their own logic and feed back into one another. Work doesn’t move in a straight line; it loops. We’d love to believe every flow cell clears QC on the first pass, but the reality is messier — flow cells that requeue or need a top-off (10–20% is normal), samples that fail QC on poor quality, workflows that branch and reflex — and each one sends work back upstream, resetting what the downstream systems expected. That looping is the part almost no one engineers. The instrument works. The software runs. The assay is validated. What fails is the space between them — the handoff from wet to dry lab, the integration from LIMS to LIS to EHR, the assumption one system makes about another that no one ever wrote down. Across a decade and hundreds of labs, the most expensive failures I watched weren’t broken components. They were broken interactions — emerging from how the pieces were wired together, owned by no single person. Modeling the system as a whole — tracing how one decision propagates and feeds back through every other before the contract is signed, so labs can make the right end-to-end calls the first time — is what I built HelixWrks to do.
Starting where the system ends — and learning what happens when it breaks
My career began not in diagnostics but in clinical genomics, at one of the largest commercial clinical genomics labs in the world at the time. I sat at the final checkpoint before patient reports went out the door — validating clinical sequencing data against patient records, identifying errors before they became consequences. I worked across the full breadth of genomic methods: NGS, Sanger, MLPA, gel electrophoresis, long-range PCR, secondary and tertiary analysis. I also got a front-row seat to the billing and reimbursement machinery — and the operational weight of running a clinical genomics lab certified under some of the strictest combined state regulatory frameworks in the country, where those requirements shape every workflow decision upstream.
What I was really doing, without the vocabulary for it yet, was learning the entire end-to-end genomics workflow from accessioning to report — and how tightly each part of it was coupled to the rest. I saw the operational reality of commercial scale: sample quality variance, instrument downtime, the constant throughput pressure of a lab processing tens of thousands of samples a month. I saw doctors ordering the wrong tests for their patients because genomic testing literacy hadn't caught up to the menu. I saw a whole team dedicated to variant analysis, and I learned how critical disciplined reclassification processes are for clinical genomics. Finally, I saw how the complexity of clinical genomics reimbursement shaped every decision the lab made about scale and growth.
All of it held together by a level of custom engineering and operational sophistication that people outside clinical genomics routinely underestimate. Internal processes, deployment infrastructure, bespoke software solutions, and custom scripts spanning every interface between accessioning, instrument, analysis, and report — running a commercial clinical genomics lab at that scale meant building and maintaining a stack far beyond what any off-the-shelf ecosystem could deliver. That was true in 2014, and it's still largely true today. The tooling has matured, the standards have improved, and more of the workflow has moved off the shelf — but the core reality hasn't changed: genomics at scale is an engineering and operations problem masquerading as a laboratory problem, and I was watching that truth from the inside before I had the vocabulary to name it.
From field implementation to global product marketing
My next role brought me into molecular diagnostics at BioFire Diagnostics, as a clinical applications specialist supporting global labs implementing the FilmArray and Torch nested multiplex PCR systems. I trained field application scientists and sales teams on the technical side of the platform, supported MDR reporting on customer complaints, and was the person on call at 2 or 3 a.m. when an instrument went down and an angry lab director needed it back before morning rounds.
That role was my real education in how smaller clinical labs actually operate: how clinical data flows from order entry through middleware to LIS to EHR, why LIS integration was so hard that platforms like Data Innovations became critical infrastructure, and what lab directors actually care about. I learned the regulatory landscape labs live inside — CLIA, CAP, IVD, IVDR, GDPR, HIPAA, FISMA, FedRAMP, 21 CFR Part 11, LDT frameworks — and why the vendors who build past regulatory minimums, for real-world usability, are the ones labs actually stay loyal to. I learned how labs plan for downtime, how they think about risk, and how the best molecular IVD brands built innovative, category-defining marketing without losing their voice.
Moving into product marketing widened the vantage point. It was the conversations I had as a product marketer — at trade shows, at user groups, in customer visits — that surfaced the growing gap between what vendors were selling and what labs thought they were actually buying. Six- and seven-figure decisions were being made on list price and vendor narrative, without any detailed modeling of total cost of adoption, integration complexity, or the operational reality the lab would inherit after go-live.
Across the full stack at Illumina — where the patterns became undeniable
At Illumina, I finally had the whole system in front of me at once. As a Senior Product Manager, I owned the lab software upstream of the sequencer — LIMS, lab automation, and the integration layers that tie sequencing into the systems around it. I ran those layers in parallel, not in sequence, and that breadth — a view across every interface of the workflow — let me see the same failure patterns I'd met earlier in my career, now at scale across hundreds of customer environments.
Later, as a Staff Product Manager, I took that view wider still — leading workflow strategy for genomics systems and the integration that runs both upstream and downstream of the sequencer. I built connectivity through a multi-pronged approach that treated every lab as its own system, working directly with clinical and research labs, and the teams implementing for them, to link sequencing platforms into EMR, EHR, LIS, and middleware environments.
Labs running clinical workflows on custom code that no one currently employed had written, couldn't fully explain, and couldn't repair without significant outside investment. Single points of failure embedded in regulated environments — maintained by institutional hope rather than institutional knowledge. Read more on laboratory technical debt →
Bench to business, every system and every interface between them — that’s the arc I’m bringing into HelixWrks, this time as products built for the people who run the lab.
Four patterns that repeat everywhere
Throughput scales linearly. Complexity doesn't. Two labs in the same market segment — same modality, same volume — can be radically different operations. Vendors routinely miss that variability, and I’ve built frameworks to make it visible. It's why so many genomics products ship with gaps customers are forced to engineer around themselves.
Most NGS onboarding delays are avoidable. A proper pre-implementation assessment — covering IT infrastructure, LIS/EHR integration, workflow mapping, and staffing — surfaces the gaps before they become project-killing surprises that erode the business case entirely.
Labs that can't quantify their value to leadership look like cost centers — regardless of how well they're actually performing. The numbers they give to leadership are all lab COGS with no direct tie to profitability. Siloed data and fragmented order flows make the financial argument harder to build than it needs to be. The investment doesn't come, the capability atrophies, and the cycle continues.
Every lab has someone who owns the LIMS, someone who owns the instruments, someone who owns LIS integration, someone who owns the business case. No one owns the interactions between them. That’s where failures live — in the handoffs, the dependencies, the feedback loops, the assumptions one system makes about another. The absence of that whole-system ownership has been the single most consistent observation across every lab I’ve seen.
Built to solve problems I spent a decade watching go unsolved
Most people in this space own one layer. I held all of them — at once. I've been the last checkpoint before a genomics report leaves the lab. I've been in the room when implementation goes sideways. I've designed end-to-end integration strategy, managed a LIMS portfolio across hundreds of customers, and held end-to-end workflow responsibility as the PM who had to make the whole system make sense — from both sides of the table. That isn't a collection of adjacent experiences. It's a single vantage point that most people in this industry never get to develop — and it's what every HelixWrks product is built from.