Clinical Trial Data Platforms
Systems that move, process and track study data end to end, with the audit trail built in, not bolted on.
Software engineering for pharma and life sciences
Software that stands up to scrutiny.
We build scientific software and clinical data platforms for pharmaceutical teams, with 7+ years of US life-sciences delivery behind every engagement.
DiligenceFidelityConfidence
- 7+yrsof US life-sciences delivery
- Pharmaand life-sciences focus
- AWSnative architecture
- Full stackReactPythonRSAS
What we do
Engineering for the people doing the science.
From the first data file to the final audit trail, we build the systems pharmaceutical teams rely on every day.
Scientific Software Development
Applications for the people doing the science: modelling, simulation and analysis workflows with an interface a researcher will actually use.
Research Computing Environments
Multi-language environments where teams run R, Python, SAS and SQL side by side, provisioned and managed for them.
Cloud & DevOps Engineering
AWS architecture, containers, Kubernetes, infrastructure-as-code, secrets and access management.
Full-Stack Application Development
React and React Native front ends over Python and .NET back ends, on PostgreSQL or SQL Server.
Automation & Internal Tooling
CLIs, APIs, scheduled jobs and dashboards that remove the manual steps nobody should still be doing.
Our work
Systems we have built, and the problems they solved.
A look at a few of the engagements. See the full list for what we built, what was getting in the way, and what changed.
Clinical trial data management platform
Configurable pipelines move, process and track study data end to end, with the audit trail built in, not bolted on.
Read the case study →Multi-language research computing studio
A browser-based studio where researchers write and run SAS, Python, R and SQL from one interface against shared compute.
Read the case study →Redshift usage monitoring dashboard
Polls live connections and query state, then emails an admin dashboard on a schedule, no login required.
Read the case study →Role-based provisioning automation
Command-line tooling that provisions folder structures and permissions across a large research estate.
Read the case study →
How we work
A simple, disciplined way of working.
Four stages, the same every time. It keeps projects predictable and keeps your QA team comfortable.
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01
Discover
We start with the workflow, not the technology: who does the work today, where it stalls, and what an auditor would need to see.
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02
Build
Short iterations with working software in front of your users early. Code is reviewed and decisions are written down.
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03
Harden
Access control, secrets management, logging and audit history verified before anything goes near production data.
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04
Support
We stay on after go-live: monitoring, enhancements and the small fixes that keep a system trusted.
Why Dilifi
The name is the promise.
Diligence + Fidelity, delivered with confidence.
Diligence
Requirements traced, code reviewed, decisions documented. Nothing ships unverified.
Fidelity
Your data and your science arrive intact. Lineage, reproducibility and traceability are part of the design from the first day.
Confidence
A result your team, your QA group and your auditor can all stand behind.
Technology
A proven stack, chosen for the job.
We are equally at home with the open-source and commercial tools life-sciences teams already run.
Languages
- Python
- R
- JavaScript
- C#
- C / C++
- SQL
- Bash
- PowerShell
- HTML / CSS
Front end
- React
- React Native
AWS
- EC2
- EKS
- ECR
- S3
- Lambda
- Step Functions
- RDS
- Transfer Family
- Secrets Manager
Platform
- Docker
- Kubernetes
- Ansible
- Talend
Data
- PostgreSQL
- MS SQL Server
- Amazon Redshift
- Amazon RDS
Scientific
- SAS
- RStudio
- Jupyter
- Dash
- Flask
Quality
Built for regulated environments.
Pharma work has a different bar. We build with it in mind: audit history on every action, role-based access control, secrets in managed stores, traceable data movement, and reproducible environments. Seven years inside that constraint is why it is the default rather than an afterthought.
Audit history on every action
Who did what, when, and to which data, recorded automatically and reviewable in the interface.
Role-based access control
Permissions follow roles and policy, so people see the studies they should and nothing else.
Secrets in managed stores
Credentials never live in code or config files. They sit in managed secret stores with controlled access.
Traceable data movement
Follow a file from where it arrived, through each processing step, to where it ended up.
Reproducible environments
Infrastructure and environments defined as code, so what ran last quarter can be rebuilt today.
Working to a specific standard?
Tell us what your QA team needs to see and we will build to it.
Talk to us
Leadership
Safi Ahmed
DirectorDilifi Tech is led by Safi Ahmed, who builds full-stack systems end to end: React and React Native front ends, Python and .NET back ends, the databases beneath them and the AWS infrastructure they run on. His work has centred on clinical study data management and scientific computing platforms. He holds a Master’s in Management Information Systems.
Contact
Let’s talk about what you’re building.
Tell us about the study, the workflow or the system. We will come back to you personally.