Industries
Research & Science
Brilliant models.Fragile pipelines?
When key people leave, scientific work must not depend on local scripts, missing lineage or unclear environments. alpLytics builds research infrastructure that keeps analyses, models and publication data traceable.
The Reality in Research
Excellent research loses impact when results cannot be traced.
Many research groups work with grown scripts, local environments and distributed data stores. This works for individual people, but fails during publication, handover, scaling and long term scientific use.
Analyses are scientifically strong but hard to reproduce
Notebooks, scripts and dependencies often live locally or across project folders. When people change or systems are updated, it becomes unclear which code created which result.
ASSESS maps analysis paths. ENGINEER moves critical workflows into versioned and documented environments.
Models stay prototypes despite scientific value
A trained model is not yet a usable system. Without operations, monitoring, data versioning and clear handover, it remains a demonstration rather than a reliable application.
DESIGN defines the path from model to operation. ENGINEER builds interfaces, monitoring and controlled updates.
Raw data without governance or documentation
Measurement data, survey results, simulations and external datasets change throughout project lifecycles. Without metadata, versions and clear responsibilities, a dataset quickly becomes an interpretation risk.
DESIGN defines governance and metadata logic. ENGINEER builds a research data model following FAIR principles.
Our Approach
From fragmented analyses to reliable research infrastructure.
Research infrastructure must enable exploration while preserving traceability. The ADET framework combines scientific flexibility with technical stability, documented lineage and clear handover.
Understanding research paths
We analyse data sources, analysis environments, scripts, models and documentation. Interviews with research teams, IT and project leadership show where reproducibility, access and collaboration currently slow progress.
Data and infrastructure audit with prioritised action areas
Architecture for reproducible science
We design an architecture for versioned environments, central data storage, metadata, experiment tracking and model versioning. The focus is scientific traceability and practical usability.
Technical concept, tool recommendation and FAIR data model
Building pipelines and model operations
We build automated data pipelines, reproducible analysis environments and model operations. When a model is used, we deliver an interface, monitoring and controlled updates with it.
Reproducible pipeline, model service, monitoring and documentation
Handover to the research team
Research teams and IT receive role based onboarding. The documentation explains usage, operation and extension so the infrastructure can be maintained after the project ends.
Operations manual, onboarding material and four weeks of hypercare
Understanding research paths
We analyse data sources, analysis environments, scripts, models and documentation. Interviews with research teams, IT and project leadership show where reproducibility, access and collaboration currently slow progress.
Data and infrastructure audit with prioritised action areas
Architecture for reproducible science
We design an architecture for versioned environments, central data storage, metadata, experiment tracking and model versioning. The focus is scientific traceability and practical usability.
Technical concept, tool recommendation and FAIR data model
Building pipelines and model operations
We build automated data pipelines, reproducible analysis environments and model operations. When a model is used, we deliver an interface, monitoring and controlled updates with it.
Reproducible pipeline, model service, monitoring and documentation
Handover to the research team
Research teams and IT receive role based onboarding. The documentation explains usage, operation and extension so the infrastructure can be maintained after the project ends.
Operations manual, onboarding material and four weeks of hypercare
Results from the Field
What changes in research institutions.
Critical analyses remain traceable and executable after staff changes, project end or system changes.
Lineage, code version and analysis environment are documented so reviews and follow up projects can rely on them.
Handovers to new researchers, IT or project partners become predictable because data, code and model state are documented together.
Analysis paths lived in local scripts, data versions were distributed and central results could only be reproduced by individual people.
alpLytics built versioned analysis environments, documented lineage and reproducible pipelines.
Research teams, project leadership and IT can review, continue and hand over results for review or follow up projects.
Research Integrity Workflow
From raw dataset to a traceable research result.
The workflow shows how data provenance, analysis path and model operation work together so research results remain reliable even months later.
Version analysis
Code, environment and parameters are documented together so results remain traceable later.
Relevant Services
What we bring to research institutions.
Next Step
Which analysis cannot be reproduced today?
In 30 minutes we identify which analysis, pipeline or model path carries reproducibility risk and what reliable infrastructure could look like.