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We optimize analytics data workflows, help adopt data standards, automate regulatory deliverables, and migrate you to validated open-source solutions or help you adopt a multilanguage setup while automating the most time-intensive parts of your process.
From operational speed to strategic positioning, our automation
and migration work delivers impact at every level.
We eliminate the repetitive, time-consuming manual work that keeps your teams from higher-value analysis. Submission dossier creation for ICH eCTD modules — including Clinical Summary and Clinical Study Reports built on SDTM/ADaM datasets — becomes streamlined rather than grueling.
Open-source solutions deliver significant cost reduction while eliminating long-term dependencies on proprietary software vendors. More importantly, implementing standards now creates the foundation your organization needs to accelerate AI capabilities — the companies standardizing today will be the ones deploying AI-driven analytics tomorrow.
Organizations using modern open-source tooling build an innovative reputation that attracts the next generation of data scientists and statisticians. Your existing team stays engaged because open-source expertise is transferable, career-building, and keeps them working with tools the broader industry is adopting.
We sit on the pharmaverse council, develop core packages, and contribute directly to the ecosystem your teams depend on. Working with Appsilon gives you access to deep connections, collaborative innovation, and efficiency gains from co-developing methods and statistical software alongside the broader community.
From R and Python ecosystem work to AI-driven TLF generation, we cover the entire analytics modernization lifecycle.
Package development, testing, documentation, open-releasing and validation across your analytics ecosystem.
Best engineering practices applied
to existing fragmented workflow
and analytics infrastructure.
Working with CDISC datasets, generating metadata, and implementing analysis
results standards.
Leverage AI for automated Tables, Listings,
and Figures generation from
standardized data.
Generating insights from a repository
of harmonized CDISC data
from past studies.
Building interactive dashboards
for performing Medical Data Review
using the raw CRF data.
Setting up and optimizing Git workflows
for version control across your
analytics teams.
End-to-end support for submission
dossier creation aligned with
ICH eCTD requirements.
Structured migration from proprietary
tools to validated open-source
R and Python solutions.
We bring ecosystem access, regulatory experience, and elite talent that compounds your capabilities.
We sit on the pharmaverse council, develop core packages, and contribute to the ecosystem. You gain access to our deep connections and collaborative innovation.
Partnered with Eli Lilly and R Consortium on pilot FDA submissions. We know what regulators expect and how to deliver it with open-source tooling.
We've helped 8 of the top 10 pharma companies modernize their analytics workflows. We understand the regulatory constraints, organizational dynamics, and technical debt you're working with.
Appsilon engineers serve as core contributors to the {teal} framework, sponsored by Roche. Our team shapes the tools your teams rely on.
Read how Appsilon helped a top 50 pharmaceutical company design a custom system for analytics in R and Python, saving the client $930,000 annually.








Data Engineering Lead
Top 10 Pharma Company
Associate Director
Top 50 Pharma Company
Human Resources People Partner
Top 10 Pharma Company

Find clear answers to common questions about GxP compliance, helping you navigate regulations with confidence.
Timelines vary based on the scope of your existing SAS codebase and the complexity of your regulatory workflows. A focused pilot — migrating a single study's TLF pipeline — typically takes 8–12 weeks. Full-scale enterprise migrations are phased over 6–18 months, running parallel to existing SAS workflows to ensure zero disruption to ongoing submissions.
Yes. The FDA has publicly stated that they accept submissions using R. Appsilon partnered with Eli Lilly and the R Consortium on pilot FDA submissions that demonstrated R-based workflows meet regulatory expectations. The key is proper validation — which is exactly what our frameworks like Axon.R are built to provide.
We never recommend a hard cutover. Our migrations run in parallel — your SAS environment stays fully operational while we build, validate, and QC the open-source equivalent. Teams transition gradually, often starting with new studies on the R/Python stack while existing studies complete on SAS. License costs reduce naturally as adoption shifts.
Every solution we deliver follows a validated, audit-ready process. Our Axon.R framework provides GxP-aligned R package validation with full traceability. We implement IQ/OQ/PQ protocols, maintain validation documentation, and work within your existing quality management system. Our engineers have direct experience with regulatory inspections and know what auditors look for.
Absolutely. Most of our pharma partnerships start with a scoped pilot — a single workflow automation, one study migration, or a proof-of-concept dashboard. This lets you evaluate our team, our process, and the ROI before scaling. The pilot typically delivers enough measurable value to build the internal business case for broader adoption.
We're not just consultants — we're core contributors to the tools the industry uses. Our engineers build and maintain key pharmaverse packages, contribute to the {teal} framework sponsored by Roche, and sit on the pharmaverse council. When you work with us, you're working with the people who shape the ecosystem, not just use it.
From custom dashboards and applications to AI-powered solutions and compliant computing environments, our engineers and infrastructure architects accelerate clinical development within fully validated, regulatory-compliant frameworks.