TealFlow

Clinical Trial Apps in Hours, not Weeks

TealFlow combines trusted {teal} modules with AI to generate interactive, validated clinical trial analysis apps in minutes - no coding required. Get a working prototype instantly.

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The Fastest Path to Clinical Trial Apps

See TealFlow in Action - watch how an app is built in minutes.

Chat-based creation

Build apps with simple dialogue.

Rapid prototyping

From idea to a POC in minutes and a full app within hours.

Human-in-the-loop

Ensures quality and compliance.

No AI hallucinations

Only configures trusted {teal} modules

Long validation cycles cause delayed submission.
‍
SAS-only lock-in makes scaling expensive and difficult.
‍
Shadow systems outside validated
control create audit risk.
‍
Manual, duplicated environments slow submissions.

Legacy systems are slow, complex and exclusive.

Clinical research teams are drowning. Traditional clinical app development takes several months, requires deep coding expertise, and leaves non-programmers waiting on dev teams. Backlogs grow, filings are delayed, and valuable insights sit idle instead of guiding decisions. The result? Teams lose speed, efficiency, and competitive edge. 

Delays

15+ weeks for TFLs and reports means insights arrive too late.

Bottlenecks

Dev teams are overwhelmed, scientists wait in line.

Complexity

{teal}/R requires specialized skills; not every scientist can use it

Value lost

Every delay slows filings and puts millions at risk

Why Appsilon?

Use Cases for TealFlow

Value across every level of your clinical data organization.

For Business Users

Prototype apps independently via natural language commands—no coding required.

Create insights in minutes, not weeks

For Developers

Generate clean, review-ready code.

Skip repetitive setup; access production-ready code that’s immediately deployable.

For Leadership

Accelerate filings, reduce timelines, and unlock operational efficiency. Empower business users to generate insights directly, reducing developer bottlenecks.

Our Clients About Appsilon

How It Feels to Work with Us

"Appsilon's control, understanding and continuous improvement of our Posit infrastructure leaves us free to focus on domain."

Andrea Nicolaysen Carlsson

Technology Manager Electrodes at Elkem ASA

"Delivery - excellent, proactive, focused on value. Very good on all levels. Proactive support from Delivery Manager is outstanding."

Director

at Top10 Pharma Company

"Proactive and bringing in fresh ideas on a technical and workflow level. Valuable members of the team who actively contribute to achieving goals."

Director

at Top5 Pharma Company

Talk to Our Experts

Let’s Build Your Data Science Environment

Appsilon Experts
Partner with our experts to design, optimize, and manage cloud infrastructure that grows with your business needs.
Rafael Pereira
Platform Unit Lead
We will contact you within 24 hours!
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FAQs About SCE

Answers to the most common questions about data platforms, cloud solutions, and infrastructure best practices.

Talk to our Experts

Data governance ensures data quality, security, and regulatory compliance, enabling organizations to make accurate decisions and maintain trust in their data assets.

Infrastructure as Code is the process of managing and provisioning IT infrastructure using code instead of manual configuration. It allows for automation, version control, and scalability, ensuring consistent and repeatable infrastructure setups.

Cost optimization involves right-sizing your infrastructure, using cloud cost-management tools, automating workflows, and leveraging serverless or spot instances to reduce idle resource usage and improve efficiency.

A Scientific Computing Environment (SCE) is a specialized infrastructure for managing complex data analysis and modeling, particularly in data-intensive industries like life sciences and pharma.

CI/CD pipelines automate code integration and deployment, reducing manual errors, improving delivery speed, and ensuring consistent updates for data-driven applications.

Data orchestration automates the movement and transformation of data across systems, improving accessibility, consistency, and readiness for analytics.