Stuyvenberg Advisory Group

Most trial tech teams find their regulatory gaps later than they should.

SAG helps clinical trial technology companies identify the documentation, standards, and AI requirements missing from their product spec, before they become expensive to fix.

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The gap

The gaps that slow trial tech teams down aren't usually technical. They're requirements that were never translated into the product spec.

Most trial tech products are built to solve a real clinical problem. What gets missed are the requirements that live at the intersection of regulatory expectations and product decisions. They're almost always cheaper to address before the build than after.

Documentation and evidence gaps

Validation evidence, audit trail specifications, and change control documentation aren't compliance paperwork. They're product requirements. When they're missing from the PRD, they get retrofitted later at significantly higher cost, or they surface in a sponsor conversation at exactly the wrong moment.

Clinical data standards and interoperability

How your product handles data, how it maps to CDISC domains, how it interoperates with downstream systems, what transformations it applies and where those are documented, shapes what sponsors can actually do with your output. These decisions belong in the product spec, not the integration guide.

AI feature requirements in regulated contexts

Introducing an AI feature into a regulated trial context carries a specific set of requirements: appropriate training data, human-in-the-loop design, explainability, change control for model updates, post-launch performance monitoring, and documentation of all of the above. Missing any one of them is a gap a sponsor's quality team will find.

PRD gaps that compound over time

The most expensive regulatory problems in trial tech aren't failures. They're omissions: requirements that weren't captured early enough, decisions that seemed low-stakes at the time, and standards considerations that got deferred. Finding them before engineering locks them in is what makes them fixable.

Services

Engagements built around what your team can act on.

Every engagement starts with understanding where your product stands. What comes out the other side depends on what's most useful: typically a prioritized gap assessment, and sometimes additional artifacts for the product or engineering team to build from.

Signature offering

SIGNAL™ Review

A structured assessment of your product against the regulatory and standards dimensions that matter most in trial tech: clinical data standards alignment, documentation and evidence approach, AI feature requirements, software qualification scope, privacy, and human factors for investigator and site workflows. You get a clear picture of where you stand and what to address first.

Training

Regulatory-Ready AI for Trial Tech Teams

The flagship workshop: a 60-to-90-minute working session for product, design, and engineering leads on building AI-enabled features in regulated trial contexts. Other sessions available on clinical data flow and standards alignment. Format is interactive. Teams bring a real feature and we work through it together.

Ongoing

Fractional Advisory & Retainer

Ongoing regulatory-to-product translation across your roadmap: async PRD and requirements review, office hours for product and engineering, and continuity across releases. Scoped as a monthly retainer. Built for teams that need a standing translator without a full-time hire, especially during active development or pre-commercial scaling.

About
Jessica Stuyvenberg

Jessica Stuyvenberg

Founder & Principal
Stuyvenberg Advisory Group

I know what pharma data management teams ask, because I worked in that ecosystem.

Before founding SAG, I spent over a decade building regulated clinical data and AI-enabled products. That work included building and shipping products across the full clinical data pipeline: upstream study design with biomedical concept encoding, SDTM conversion tooling, and downstream data quality analysis. The work predates and runs parallel to the industry's DDF and USDM initiative.

It also included leading AI product development in regulated trial contexts: protocol authoring and eConsent tooling built through iterative collaboration with biopharma organizations who asked the same questions your pharma buyers bring to pilot conversations. How does this map to our standards, what does your validation package look like, and what happens when the model changes.

"The gap isn't compliance knowledge or product knowledge. It's translation between them, at the moment in the build when it's still cheap to get right."

SAG is the practice I launched to bring that translation to trial tech teams who need it at the product level, not after the pilot stalls.

B.S. Biomedical Engineering, Johns Hopkins University

Provisional surgical device patent and co-authored clinical research

10+ years building regulated clinical data products and AI-enabled tools in life sciences environments

Hands-on experience across SDTM pipelines, USDM-aligned protocol tooling, and eConsent in regulated contexts

Working familiarity with 21 CFR Part 11, ICH E6(R3), CDISC standards, FDA AI/ML guidance, and software qualification in regulated trial contexts

Contact

Let's find out if the fit is real.

If you're a clinical trial technology company preparing for pharma pilots, navigating investor diligence, or building AI-enabled features in a regulated context, and you don't have regulatory-to-product translation in-house, start with a conversation. No proposal until we both agree it makes sense.

Email jessica@stuyvenbergadvisorygroup.com LinkedIn linkedin.com/in/jessica-stuyvenberg
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