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Use Machine Learning to Improve Decision Making
Trusted by:
Build new capabilities with end-to-end support from Appsilon — from defining the need, through data collection and modeling, to deploying models in production applications.
Custom AI Models Deployed at Your Fingertips
We build AI-powered decision systems utilizing computer vision, NLP, and machine learning to enable automatic analysis and insights.
Design & POC
Analysis of data quality, assessment of potential, and building a proof of concept to validate the approach before scaling.
Streamline Data
Data capture, ETL implementation, data integration, and automated workflows to feed your models with clean, reliable data.
Train & Optimize Models
Dataset curation for machine learning, architecture design, model training, and optimization for production-grade performance.
How We Helped Our Partners
Impacting Pharmaceutical Supply Chain: Empowering Planners with Business-Critical Shiny Apps
Discover how an international pharmaceutical company dramatically improved their data analytics and decision-making capabilities through optimized R Shiny app development, enabled by strategic collaboration with Appsilon.
End-to-End AI Capabilities
From data pipelines to deployed models — everything you need to build production-grade decision support systems.
Vision
Computer Vision Systems
Automatic image recognition and analysis powered by deep learning for quality control, drug discovery, and research.
Data
Data Pipeline Design
End-to-end data capture, ETL implementation, integration, and automated workflows to feed production models.
ML
Model Training & Optimization
Dataset curation, architecture design, hyperparameter tuning, and optimization for production-grade performance.
Interface
Interactive Dashboards
User-friendly dashboards and reports that summarize AI-generated insights for business teams and decision makers.
Speed
Real-Time Processing
Process data in real time to enable immediate reactions to findings and faster decision-making cycles.
Deployment
Scalable Deployment
Containerized deployment on cloud or on-premise infrastructure, with monitoring, versioning, and retraining pipelines.
#Why Appsilon
Not just a vendor.
A force multiplier.
We bring ecosystem access, regulatory experience, and elite talent that compounds your capabilities.
Pharmaverse Council Members
We sit on the pharmaverse council, develop core packages, and contribute to the ecosystem. You gain access to our deep connections and collaborative innovation.
Proven with Regulators
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.
Real Pharma Experience
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.
Core Open Source Contributors
Appsilon engineers serve as core contributors to the {teal} framework, sponsored by Roche. Our team shapes the tools your teams rely on.
From Idea to Production AI
We build AI-powered decision systems utilizing computer vision, NLP, and machine learning to enable automatic analysis and insights.
Week 1-3
Design & POC
Assess data quality, explore feasibility, and build a proof of concept to validate the approach before scaling.
Week 4-8
Data & Model Development
Build data pipelines, curate training datasets, design model architecture, and train and optimize models.
Week 9-12
Application & Deployment
Build user-facing dashboards, integrate model outputs, and deploy into production infrastructure.
Ongoing
Monitoring & Iteration
Track model performance, retrain on new data, expand capabilities, and scale to new use cases.
#Valued by clients worldwide
#Contact Form
Consult with Our Experts for Custom Solutions
Get in touch
with Appsilon Experts
with Appsilon Experts
We will contact you within 24 hours!
#Our Clients About Appsilon
How It Feels to Work with Us
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 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
We wouldn't be here where we are now without Appsilon. I want this partnership to keep going and growing.
Human Resources People Partner
Top 10 Pharma Company
Expert Insights
Insights, Inspiration & Distilled Knowledge
Scaling Decision Support Systems: When to Use React, Python, and R
Shiny, React, or Python? Learn how to choose the right stack for building and scaling decision support systems.
Piotr Pasza Storożenko
Read more
Decision Making Support Systems #3: Differences between IA and AI
What are the differences between AI and IA? When is Intelligence Augmentation superior to a pure Artificial Intelligence approach? Which solution makes the most sense for your problem set? Consider IA solutions when a limited amount of data exists
Appsilon Team
Read more
Decision Support Systems #4: How to Implement an IA Solution
To achieve IA implementation success, plan for a partnership between humans and machines, not replacement. Buy-in is crucial, of course. So educate your workforce about IA and plan for hero moments. Automate tasks, not jobs and start small.
Appsilon Team
Read more
Ebook for Clinical Data & Platform Leaders
Your Guide to Building a Better SCE
Learn how pharma companies are building validated, flexible platforms to support both regulatory submissions and modern analytics.
FAQs
Find clear answers to common questions about GxP compliance, helping you navigate regulations with confidence.
01.
What industries do you serve with AI solutions?
We have experience in life sciences, pharma, entertainment, biodiversity, manufacturing, and more — building computer vision and ML systems at scale.
02.
Do you handle the full ML pipeline or just model training?
We provide full end-to-end service: from data collection and ETL, through model training and optimization, to deploying models in interactive applications.
03.
What technologies do you use for AI projects?
Python, PyTorch, TensorFlow, cloud platforms (AWS, Azure, GCP), Docker, Databricks, DVC, and neptune.ai for experiment tracking.
04.
How do you make AI model outputs accessible to business teams?
We build interactive dashboards and reports that summarize AI-generated insights in a user-friendly format for non-technical stakeholders.
Contact Us
Drive Impact with Appsilon
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.