Scattered PDFs, spreadsheets, and data sources
A validated, research-ready dataset
Documents and files are collected, extracted, reconciled, and documented, so analysis starts from data your team can defend.
Research Data & Technology Services
Research data engineering and software for rigorous research.
Acadalytics helps research teams acquire, organize, validate, analyze, and operationalize complex data—through reproducible engineering and software built around their workflows.
For research labs, consultancies, and research-led organizations.
Sources
- PDFs and reports
- Spreadsheets and survey files
- Authorized APIs
Extract & harmonize
- Structured extraction
- Common schema and codes
- Documented mapping
Validate
- Automated checks
- Source reconciliation
- Exceptions for review
Deliver
- Research-ready dataset
- Data dictionary
- Versioned code and runbook
Where we help
From scattered inputs to dependable research infrastructure
Three situations we see often, and what a good outcome looks like.
A brittle, manual research process
A reproducible, automated pipeline
Steps that depend on one person's spreadsheet become versioned code that anyone on the team can re-run and review.
Disconnected monitoring data
A traceable, maintainable data system
Program and survey data flow into one structure where every indicator links back to its definition and its source.
Services
Four service areas for research teams
Each is scoped around your workflow and delivered with documentation.
Research Data Engineering
Acquire, extract, clean, harmonize, and validate research data in pipelines your team can re-run.
- Document and PDF extraction into structured tables
- Cleaned dataset with data dictionary
- Versioned, re-runnable processing code
Research Analytics & Statistical Computing
Statistical programming and reproducible analysis workflows, implemented and documented around your research design.
- Analysis-ready datasets and scripts
- Tables and figures generated from code
- Model scripts with diagnostics
Research Software Engineering
Custom research applications, APIs, and data tools that are tested, documented, and built around how your team works.
- Analytical web applications and data apps
- APIs and system integrations
- Automated tests and technical documentation
Research Monitoring & Evidence Systems
Monitoring databases, survey data QA, validated indicators, and reporting automation with clear traceability.
- Validated indicator pipelines
- Survey and program data quality checks
- Dashboards and automated periodic reports
Methods beyond routine statistical programming, such as econometrics and causal inference, are taken on only where qualified methodological expertise is in place.
Who we help
Built for research-active teams
We work with teams that need technical capacity beyond routine analysis, without building a full engineering department.
Research consultancies
Technical capacity for defined client work packages.
University research centres and labs
Dataset preparation, reusable code, and software tools for research groups.
Research-active NGOs and MEL teams
Data quality, indicator pipelines, and reporting systems for monitoring, evaluation, and learning.
Individual funded researchers
Well-scoped technical tasks, reproducible analysis, and custom tools.
Evidence
Delivery you can verify
We describe our working practices here. Published client work and demonstrations appear on the Our Work page only when they are documented and approved.
Source-to-output checks
Outputs are checked against the documents and files they came from, and the results of those checks are reported.
Versioned code
Processing and analysis live in version control, so each result can be traced to the code that produced it.
Documentation you can use
Data dictionaries, runbooks, and acceptance checklists let your team re-run and maintain the work.
Our approach
A clear process from first conversation to handover
Access to client data is subject to agreement. Maintenance and support apply only where the contract specifies them.
- 1
Discover & scope
We discuss your goals, data, and deliverables, and describe what we can and cannot do. Data access is subject to agreement.
- 2
Implement
Work proceeds in small steps, starting with a pilot on a sample, and you see progress along the way.
- 3
Validate & review
Automated checks and a manual review sample confirm the outputs against your definitions and sources.
- 4
Document & hand over
You receive the outputs, code, and documentation. Ongoing support is provided only where the contract specifies it.
Founder
Aditya Tomar
Aditya is a data engineer and the founder of Acadalytics. He started the company to connect research questions with the specialized data technology they depend on.
Acadalytics is founder-led, and the aim is to become a trusted research engineering partner.
About AcadalyticsFAQ
Practical questions
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.
Can we hire you for a small project?
Often yes, if it is well scoped. Send a short project summary and we will tell you honestly whether it is a good fit and what a sensible first step would be.
What does handover include?
The agreed outputs, such as datasets, code, or software, plus the documentation your team needs to run and maintain them: a data dictionary, a runbook, and test or validation results where relevant. Any ongoing support is defined in the contract.
How is sensitive data handled?
We assess permissions and controls before accepting sensitive or restricted data. Please do not send sensitive datasets or participant data in the initial inquiry form.
How are projects scoped and priced?
After a short discussion of goals, data, and deliverables, we propose a written scope. Pricing depends on that scope and is agreed in writing before work begins. We do not publish fixed prices.
Tell us what your research workflow needs.
Share the goals, the data types, and the technical help you need.