Study applications & data collection
Purpose-built web applications for tasks, questionnaires and structured data capture, designed around your protocol and participant journey.
Research & data science
From raw data to results you can reproduce.
Data science, analysis, research pipelines, study applications and HPC for research teams. Built around your methods and documented, so your team can rerun and extend the work.
- Versioned from the start
Code, parameters and environments are kept under version control, so a run can be traced.
- Checks you can inspect
Validation steps and quality reports sit alongside the results, not in someone’s head.
- Documented for your team
Run instructions and handover notes, so the work doesn’t depend on us.
- Your methods, implemented
We build the approach your team defines and flag assumptions for review.
Where we can help
Anything from one study app to a full HPC workload.
Bring a specific problem or a wider programme of work. We’ll build one component or connect the whole research workflow.
Data analysis & dashboards
Explore datasets, implement agreed analytical methods and build visual tools that help your team investigate results.
Reproducible data pipelines
Bring source data into a consistent form, track transformations and make recurring processing easier to inspect and rerun.
LLM experimentation & evaluation
Build pipelines for prompt experiments, batch queries and evaluation, with run metadata, cost visibility and reviewable outputs.
HPC & complex computation
Prepare and run demanding jobs on suitable HPC or cloud infrastructure, with attention to resources, dependencies and recovery.
Not sure where yours fits?
Tap through a few questions and the solution finder sketches a blueprint for your study or pipeline.
Try the solution finderExample workflow
What happens to your data at each stage.
Select a stage to see its inputs, deliverables and checks. This is an example: your study sets the actual approach.
01Collect
Start with the right information.
- Inputs
- Study requirements, existing records and source formats.
- Deliverables
- A study application or collection interface with structured exports.
- Checks
- Pilot the participant journey and agree access, consent requirements and data fields before collection.
02Validate
Understand what you can trust.
- Inputs
- Incoming responses, files and metadata.
- Deliverables
- Quality reports and a traceable dataset ready for processing.
- Checks
- Check missing values, duplicates and formats. Flag exceptions for researcher review.
03Process
Make complex work repeatable.
- Inputs
- Validated data, processing rules and available infrastructure.
- Deliverables
- Versioned pipelines and scheduled or batch computational jobs.
- Checks
- Record parameters, dependencies and run status. Check small representative runs before scaling.
04Analyse
Turn your methods into working code.
- Inputs
- Prepared datasets, research questions and agreed methods.
- Deliverables
- Analysis code, model evaluations and documented outputs.
- Checks
- Review assumptions, comparison methods and limitations with the research team.
05Explore
Make the results usable.
- Inputs
- Validated analysis outputs and the questions people need to ask.
- Deliverables
- Dashboards, interactive tools and reproducible export packages.
- Checks
- Check visualisations, definitions and access controls. Document how to refresh and interpret outputs.
Working together
Built with you, documented and handed over.
- 01 / Define
Requirements you can review
Agree the research workflow, data handling requirements, infrastructure and acceptance checks.
- 02 / Build & validate
Working tools, tested with you
Review applications and pipelines in increments, using representative inputs and agreed validation cases.
- 03 / Document & hand over
Knowledge stays with your team
Receive documented environments, run instructions and a clear plan for ownership, maintenance and support.
Next step