Services
Four connected service areas that take research data from raw inputs to dependable evidence, analysis, and software.
Choose the area closest to your problem, or send an inquiry and we will help you decide. Each service is built on the same standards: traceable, reproducible, documented, and reviewable.
The idea
Research data work tends to be split across people and tools: someone extracts, someone cleans, someone analyzes, someone builds a dashboard. Each handoff is a place where meaning gets lost and traceability breaks.
Our four services cover the whole path. Data engineering produces trustworthy data. Analytics turns it into reproducible results. Software makes the process usable by your whole team. Monitoring systems keep evidence flowing and traceable over time.
You can use one service on its own, or several together. In every case, the aim is the same: work you can explain, check, and re-run.
Service areas
Every service page covers scenarios, capabilities, artifacts, workflow, quality checks, and limits.
Turn documents, files, and fragmented sources into validated datasets, with pipelines your team can re-run and defend.
Statistical programming and reproducible workflows: analysis-ready data, model scripts, and tables and figures generated from code.
Custom research applications, APIs, and data tools that are tested, documented, and built around how your team works.
Monitoring databases, survey data QA, validated indicators, and reporting automation, with every number traceable to its source.
Choosing a service
| If this describes you | Start with | Typical output |
|---|---|---|
| My data is scattered across documents and files | Research Data Engineering | A validated dataset, data dictionary, and re-runnable pipeline |
| I need analysis I can reproduce and defend | Research Analytics & Statistical Computing | Analysis code, documented assumptions, and regenerable tables and figures |
| My team needs a tool, API, or platform | Research Software Engineering | A tested, documented application deployed as agreed |
| We report indicators from program or survey data | Research Monitoring & Evidence Systems | A traceable indicator system with dashboards and automated reports |
Shared standards
Provenance is built in. You can follow any value in the final dataset back to the file, page, or record it came from.
Cleaning, recoding, joining, and imputing are explicit steps in code and in a mapping document, not manual edits nobody remembers.
Validation results ship with the delivery, including the failures and the cases we could not resolve. We do not hide limitations.
Pinned environments and documented steps mean the same inputs produce the same outputs, today and next year.
Your researchers decide what a variable or indicator means. We implement it faithfully and flag ambiguity instead of guessing.
Dictionaries, runbooks, and READMEs are part of the deliverable, so your team can operate the work without needing us in the room.
Ways to start
Timing, scope, and terms are agreed in writing.
You send a short summary. We reply with questions and an honest view on whether we are the right partner, including when we are not.
We prove the approach on a small slice of your data or workflow before anyone commits to the whole. You see the method, the checks, and the output quality first.
A written scope with deliverables, acceptance criteria, and data-handling terms. You know what you are getting and how you will accept it.
If the work needs to keep running, maintenance and support can be added in the contract. We do not assume it, and we do not leave it vague.
Process
We discuss your goals, data, and deliverables, and tell you what we can and cannot do. Access to data is subject to agreement.
Work proceeds in small steps, starting with a pilot on a sample, and you see progress along the way, not just at the end.
Automated checks and a manual review sample confirm the outputs against your definitions and your sources.
You receive the outputs, code, and documentation. Support after delivery exists only where the contract specifies it.
FAQ
Start with the problem. If the data is the obstacle, begin with data engineering. If the analysis must be reproducible, see analytics. If your team needs a tool or platform, see software. If you report indicators, see monitoring systems. Many projects combine two, and the inquiry form lets you choose Not sure yet.
Yes, and many do. A typical example is a data pipeline feeding a reproducible analysis, or a monitoring system that includes a custom tool. We scope them together so the pieces fit.
AI and machine learning are techniques that may be used inside a service when they suit the problem and when appropriate expertise is in place for the scope. They are not a separate offering, and we favour simpler methods when those are enough.
Not on our own. Full research design and evaluation need subject specialists. We build the data and software around the work and can collaborate with your methodologists.
It depends on scope, which we agree in writing before any work begins. We do not publish fixed prices because the range of research data problems is too wide for a price list to be honest.
Describe your project and we will point you to the right starting point, or tell you honestly if we are not it.