Research Data & Technology Services
Acadalytics is the engineering partner behind research teams. We turn scattered documents and fragmented data into validated datasets, reproducible analysis, and dependable software, so researchers can focus on the evidence.
Send a short brief and get a candid view on fit. No commitment.
What Acadalytics is
Acadalytics is a research data and technology firm. We acquire, organize, validate, and analyze complex data, and build the software around it, so your team can put evidence in front of reviewers and funders with confidence.
When a reviewer or funder asks where a figure came from, you can show them. Every value links to its source file, page, or record.
Your analysis stops living on one laptop or in one person's memory. Versioned, tested code lets anyone on your team rerun it.
Every delivery lists the checks that failed and the issues we could not resolve, so nothing surprises you after publication.
Your researchers define the variables, indicators, and models. We make them executable and flag any step beyond our expertise.
Services
Each is scoped around your workflow, built with checks, and delivered with documentation you can use.
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.
Methods beyond routine statistical programming, such as econometrics and causal inference, are taken on only where qualified methodological expertise is in place. AI techniques may appear inside a service where they fit the problem. They are not a separate offering.
Use cases
Six kinds of research data work, each with a defined way of working, defined deliverables, and checks that make the result defensible.
01
Document extraction
Turn thousands of pages of reports, filings, and publications into one structured, source-linked dataset.
Explore02
Data harmonization
Combine datasets that were never designed to fit together, with every mapping and comparability decision on record.
Explore03
Reproducible analysis
Move a manual, laptop-bound analysis into a versioned workflow that regenerates every table and figure on demand.
Explore04
Monitoring & evidence systems
Replace scattered partner spreadsheets with one system in which every reported indicator links back to its definition and records.
Explore05
Research software
Purpose-built applications, APIs, and internal platforms that fit how your researchers actually work.
Explore06
Text & document analysis
Make policy documents, transcripts, and archives searchable, classifiable, and analyzable, with humans verifying what matters.
ExploreSee it work
Most data problems hide until a reviewer finds them. Our pipelines surface them on purpose, and send them back to the source.
Interactive demonstration
Synthetic data, not a client result. Checks: assets = liabilities + equity, and no missing values.
| Entity | Assets | Liabilities | Equity | Result |
|---|---|---|---|---|
| Synthetic Co. A | 1250 | 780 | 470 | Not checked |
| Synthetic Co. B | 980 | 610 | 370 | Not checked |
| Synthetic Co. C | 1500 | 900 | 560 | Not checked |
| Synthetic Co. D | 720 | 455 | 265 | Not checked |
| Synthetic Co. E | 640 | 400 | — | Not checked |
| Synthetic Co. F | 410 | 150 | 260 | Not checked |
Press “Run validation” to see how failures are surfaced instead of hidden.
Where we help
Three starting points we see again and again, and the outcome we engineer for each.
Evidence trapped in documents and spreadsheets
One validated, research-ready dataset
Reports in five layouts, columns renamed every year, exports that disagree about what counts as a duplicate. We engineer that mess into a single documented dataset, and ship the checks that prove it holds together.
A manual process that lives on one laptop
A pipeline your whole team can rerun
When an analysis depends on one person's machine and memory, every reviewer question becomes a fire drill. We move it into versioned, tested code that anyone on your team can run, inspect, and rerun when the data changes.
Programme data scattered across partners
An evidence system that traces to source
Partner spreadsheets, survey exports, and programme records become one structure where every indicator links to its definition and its records. When a funder asks where a number came from, the answer is already documented.
Our standard
Six commitments that shape how we build, whatever the service.
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.
Research domains
The domains where evidence is hardest to collect, combine, and defend, and the challenge each one brings.
Panels built from filings, reports, and administrative records.
Linking surveys, facility records, and program data across years.
Assessment, enrolment, and survey data in many formats.
Large, messy time series from many sources and sensors.
Partner-submitted data and indicators across many programs.
Administrative and survey data that must be harmonized and defended.
Who we help
For teams that need more than routine analysis, without building a full engineering department.
You own the client relationship and the findings. We add specialist engineering capacity for the technical work packages inside your engagements.
Your researchers should spend their time on research. We handle the data preparation, reusable code, and tools that otherwise eat their weeks.
Your team is stretched across programs, partners, and reports. We build the data systems that make monitoring, evaluation, and learning less manual and more trustworthy.
You have a project, a budget, and a technical task that sits outside your skill set. We take it on, scoped tightly, and explain it in plain language.
Our approach
Written scope, visible progress, and a documented handover. No surprises at the end.
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.
Where we are going
Where Acadalytics is today, what comes next, and the direction we are building toward.
01 · Today
A founder-led research engineering practice. Every engagement has a named technical owner, a written scope, and checks you can read.
02 · Next
A growing library of published case studies, original technical guides, and open demonstrations, so you can judge the work before you commit.
03 · The direction
To be the research engineering partner that teams around the world call first when their data work has to stand up to scrutiny.
Founder
Founder and data engineer
Aditya is a data engineer who turns messy, overwhelming information into systems that are clear, checkable, and built to last. He founded Acadalytics so that research teams can put engineering they trust behind their work.
His standard is plain explanations, visible checks, and an honest answer early when a simpler path exists.
Read the full storyFAQ
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.
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.
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.
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.
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.
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.
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.
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.
Share your goals, your data, and the outcome you need. You will hear back with questions or a candid view on fit.