FAQ
Practical questions, direct answers
What teams ask before they start: how we work, how we handle data, and what a project actually involves.
About working with us
Who do you work with?
Research consultancies, university labs and research centres, research-active NGOs with monitoring, evaluation, and learning (MEL) teams, and individual funded researchers. If your work depends on data and you need engineering capacity you do not have in-house, you are who we built this for.
Can we hire you for a small project?
Often, yes. A well-scoped small project is a good way to start. Send a short summary and we will tell you honestly whether it fits and what a sensible first step looks like, which may be a pilot on a sample of your data.
What happens after I send the inquiry form?
We read it, and reply with questions or a candid view on fit. Nothing is committed on either side at that stage. If it looks like a match, we agree a written scope before any work begins.
How long does a project take?
It depends on the volume, variety, and condition of the data, and on how quickly definitions can be agreed. A pilot on a sample is the most reliable way to get a realistic timeline, and we give you one before you commit to the whole.
Can you work alongside our existing team?
Yes. Many engagements involve a research lead who owns the questions and methods, and Acadalytics who owns the engineering. We work in your repositories and tools where that is practical, and we document as we go so the work stays yours.
Data, ownership, and confidentiality
What does handover include?
The agreed outputs, such as datasets, code, or software, plus the documentation your team needs to operate them: a data dictionary, a runbook, a README, and validation or test results where relevant. Any support after delivery is defined in the contract.
How is sensitive data handled?
We assess permissions and controls before accepting sensitive or restricted data, and we agree handling arrangements in writing. Please do not send sensitive datasets or participant data in the initial inquiry form.
Do you sign NDAs?
Confidentiality terms are agreed in writing before you share anything sensitive. If you have a standard agreement, send it with your inquiry and we will review it.
Who owns the code, the data, and the outputs?
Your data stays yours. Ownership and licensing of code and deliverables are set out in the written agreement for each engagement, so there is no ambiguity at handover.
Can you work inside our own environment?
Where the data or your policies require it, yes. We can work within an environment you control, subject to the access arrangements we agree in writing before work begins.
Will our data be used with external AI services?
Not without your explicit written approval. Restricted or sensitive data is not sent to third-party AI services. Where AI techniques help, we agree the tools and data flows with you first.
Scope, methods, and pricing
Do you use AI?
AI and machine learning can be a technique inside a service when they suit the problem and when appropriate expertise is in place for the scope. We favour the simplest method that does the job, and we say clearly when something is a model's best guess rather than a verified value.
How are projects scoped and priced?
After a short discussion of goals, data, and deliverables, we propose a written scope with acceptance criteria. Pricing depends on that scope and is agreed in writing before work begins. We do not publish fixed prices because no two research data problems are alike.
What if our data turns out to be poor?
We tell you early. Part of discovery and any pilot is an honest assessment of what the data can and cannot support. Reporting a limitation clearly is part of the job, not a failure of it.
Do you do econometrics, causal inference, or study design?
We build the data, software, and reproducible workflows around those methods, and we collaborate with your methodologists. Methods beyond routine statistical programming are taken on only where qualified expertise is in place for the scope.
What do we need to provide?
A named person who can answer questions about definitions and data, access to the data within agreed permissions, timely feedback at review points, and clarity on what done means for your team.
Still have a question?
Send it with a short description of your project. You will hear back with an answer or a candid view on fit.