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GxP-Compliant Shiny Apps
We assess the Shiny app your teams already use, show you where it stands against GxP requirements, and hand you a costed roadmap to close the gap. Delivered alongside your developers and your QA, not around them.
Why Shiny App Validation Stalls Before Anyone Signs
Teams rarely stall because the app is bad. They stall because nobody in the room can answer the same questions: what exactly are we validating, how will we know when we are done, and what happens at the next release?
Nobody can say what "done" looks like. QA asks what is being tested and when there is enough, and four thousand lines of reactive code offer no answer.
QA arrives after the design is locked. Requirements get reviewed late and reworked at implementation rates, and the week-long review cycle repeats on every iteration.
The app is validated until something changes. Validated as one unit, a single label change re-opens the whole packet, test run, and signature loop.
Nobody can name the validated surface. Behaviour no feature claims is unsupported by construction, and most teams cannot say where that line sits.
A Feature is the Unit of Validation
A package arrives with its own unit of validation: exported functions, documentation, a test suite, a version number. An app has none of that. It is a reactive graph with no natural seam to cut along, so teams fall back on validating the whole app as one blob, or validating the package list. We work from a different unit. A feature is the closed reactive subgraph rooted at an output.
Read the full method: Validating Your First Shiny App for GxP, a Survival GuideRooted at What the User Sees
Pick something a user can see: a Kaplan-Meier plot, an adverse events table, an export button. Then collect everything upstream that has to be right for it to be right. The set is bounded, so you can enumerate it.
Matches How You Already Describe the App
Nobody writes an intended-use statement about a reactive expression. They write one about the curve for the primary endpoint, and the subgraph is scoped to exactly that.
Validate a Module Once, Reuse It Everywhere
A module's validation surface is its contract: the inputs it owns, the outputs it owns, and what it returns. Validate it on its own and every feature that instantiates it inherits that result.
Change One Module, Not the Whole App
Edit a module and only that module and the features consuming it come back into scope. The rest of the app stays where it is, which is what ends the habit of batching changes for months.
The Validated Surface Becomes Explicit
The union of your declared features is what is validated. Behaviour a user can reach that no feature claims is unsupported by construction, and an auditor can ask you which is which.
Two Numbers an Auditor Can Use
Behaviour no feature claims is a validation gap. A claimed feature no test exercises is a verification gap. An app at zero on both is fully claimed and fully exercised, and that fits on one line.
What shiny.val.tools Produces
shiny.val.tools reads your app's R source and works out what depends on what. It writes the artifacts a GxP audit expects: a per-feature reactive subgraph, the packages and functions each feature actually calls, test scaffolding, a traceability matrix, and a signable validation report. It does not run your app, and it does not run your tests. It produces evidence for someone else to validate, and the conclusion and approval sections are left for a person and preserved verbatim from then on.
Run the Tooling Against Your Own App
shiny.val.tools is open source. Point it at your app and it will produce the per-feature subgraphs, the function inventory, and a first traceability matrix before you ever talk to us.
See the Repo#How We Work
Two Phases, One Engagement
Your timeline depends on the app, the state of your environment, and your internal approval cycles. A QA review cycle alone runs about a week, which is why we bring QA in from the first week and run delivery as a milestone-based rollout with clear checkpoints.
Scoping
Scope and Access
We agree which app, which modules are in scope, and what your QA group needs to see. Access and environment approvals start here, because in regulated organizations they are usually the real bottleneck.
Phase 1
GxP Readiness Assessment
Current-state review of architecture, modules, technical debt, data flow and transformation logic. Gap analysis split by what sits at app level and what sits at platform level, a package and function inventory, and an effort estimate broken down per module so you can sequence and fund it.
Phase 2
Implementation Support
Test strategy and coverage where it matters, with regression protection for package and environment changes. A development process your QA can approve. Architecture work where structure is the blocker, and validation evidence produced from the code.
Ongoing
Support and Re-Runs
As the app changes, the evidence is re-run and the diff shows which parts have gone stale. Only the changed module and the features consuming it come back into scope, so a release stops meaning a full re-validation.
What Each Seat Gets
The App Owner
A plan that does not depend on one person's memory, and structure that makes the next developer productive.
QA and Validation Lead
Evidence in a form you can review, a process you approve rather than inherit, and involvement from the first week instead of the last.
The Budget Holder
An effort estimate per module, so scope and cost are visible before you commit, and an engagement shape that does not send you back for another approval.
IT and Platform
A clear split between what the app must satisfy and what the environment must satisfy, so the two tracks can run in parallel.
#Valued by clients worldwide
#Two Ways to Scope It
Feature by Feature, or the Whole App at Once
Feature-by-Feature Validation
Whole-App Validation
✅ Scoped Change Control
Edit one module and only that module and the features consuming it come back into scope.
❌ Full Re-Validation
A single label change re-opens the whole packet, the whole test run, and the whole signature loop.
✅ Declared Validated Surface
The union of your declared features is what is validated, and an auditor can ask you what falls outside it.
❌ Unstated Scope
Validate the app as one blob and the question of what sits inside the validated surface has nowhere to land.
✅ Function-Level Inventory for every feature.
Knowing a feature calls dplyr::filter and dplyr::semi_join and nothing else is narrower than a package list, and you can check it.
❌ Package List Only
A package list says the app loads dplyr. It says nothing about what the app does with it.
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#Our Clients About Appsilon
How It Feels to Work with Us
We rely on Appsilon's expertise to support the build and maintenance of critical clinical reporting software based in R and Shiny.
Data Engineering Lead
Top 10 Pharma Company
Appsilon team is exceptional. The engineers challenge and speak their minds, which helps do justice to the product
Associate Director
Top 50 Pharma Company
We wouldn’t be here where we are now without Appsilon. I want this partnership to keep going and growing.
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Top 10 Pharma Company
#Why Appsilon
Driven by Excellence,
Proven by Results
A trusted partner to leading pharmaceutical and life sciences organizations worldwide.
World-Class Talent
Out of 15,000+ applicants each year, only 1% are selected to join our team - a diverse group of programmers, managers, biostatisticians, and PhD-level scientists actively contributing to the industry.
Pharma Golden Standards
With over 150 projects delivered for pharmaceutical companies, you gain access to our deep, collective expertise and training support from experienced delivery managers, biostatisticians, programmers, and scientists.
Life Sciences Innovators
Our team members are creators and contributors to open-source frameworks such as Rhino, aNCA, teal, and many more—collectively downloaded nearly 500,000 times. We are active participants in Pharmaverse and R Consortium working groups.
Trusted by Global Pharma Leaders
8 out of the 10 world’s largest pharmaceutical companies have trusted our programmers and statisticians to support AI-driven workflows, analyse all types from data: from clinical and real-world data (RWD) to drug discovery experiments.
FAQs
Common questions about validating a Shiny app for regulated use, and where the line sits between what we produce and what your QA group signs.
01.
Do you validate our Shiny app for us?
No. Validation is your QA group's to own and sign. We assess the app, close the engineering gaps, and produce evidence in a form your QA can accept. The same line applies to our tooling: shiny.val.tools produces validation evidence, it does not validate your app.
02.
How is this different from R package validation?
They are adjacent layers of one stack. Package validation answers whether a package is safe to use. This answers which functions a given feature actually calls, and what the validated surface of your app is. Most regulated teams need both.
03.
Our platform qualification is not finished. Can we start?
Yes. App validation and platform qualification are separate but interdependent tracks, and they can run in parallel. Phase 1 tells you which requirements sit at app level and which sit at platform level, so neither team waits on the other unnecessarily.
04.
Our app has no tests and no framework. Is it too late?
No, and it is the common starting point. The assessment shows concretely how much each feature drags in, which is usually a clearer argument for restructuring than anything said in a meeting. The method rewards refactoring, and it is honest about what it cannot give you until then.
05.
Do we have to adopt your tooling?
No. shiny.val.tools is open source and optional. The method works without it, and the tool removes the manual effort of producing the artifacts. You can run it against your app before you contact us.
06.
What happens at the next release?
Re-run, and the diff shows which evidence has gone stale. Only the changed module and the features consuming it come back into scope, so a label change no longer re-opens the whole packet, the whole test run, and the whole signature loop.
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