Founded in 2021 by Adenle Muhsin Modupeoluwa and Adenle Fawaz Ifedolapo, Abbadh Labs was born from one clear frustration: across Africa, enormous amounts of data are collected daily by schools, hospitals, federal institutions, and organizations, yet most of it remains fragmented, underutilized, and disconnected from real-world decision-making. Instead of transforming existing data into insight, prediction, and innovation, new data is repeatedly collected while previous datasets sit unused. In healthcare, for example, patient data is gathered continuously, yet diseases and symptoms are rarely predicted before they escalate. Abbadh Labs was created to change that — turning Africa’s fragmented data into predictive intelligence that drives smarter research, healthcare, education, and national decision-making.
Turning data
into direction
Abbadh Labs is a research-technology company dedicated to transforming Africa's fragmented data into clear, predictive, and actionable intelligence that empowers researchers, institutions, and decision-makers across the continent.
Built
for a reason
Began in Academia
SABABAT 4.0 launched to make rigorous statistical analysis accessible and affordable for every undergraduate, postgraduate, healthcare professional, and data analyst — with zero hallucination and zero coding required.
Expanding into Healthcare
GenieStat applies Abbadh Labs' predictive intelligence to clinical environments — forecasting physiological decline before symptoms appear, with interpretable, patient-centred outputs built for real medical workflows.
Building for Africa
Our Unified Data Lake initiative normalizes and benchmarks African institutional data at scale — turning fragmented records into evidence for national-level decision-making across agriculture, energy, and education.
Bootstrapped and Independent
Funded through personal capital, family support, and product revenue. We have no external investors telling us who to build for. Our obligation is entirely to the researchers and institutions who use our tools.
Mission & Vision
Our Mission
To solve one of the continent's most persistent challenges: the fragmentation of data, knowledge systems, and digital tools. Abbadh Labs exists to turn Africa's scattered and often inaccessible information into intelligent solutions that individuals, institutions, and organizations can depend on daily.
Though we began in academia, our long-term scope spans research, healthcare, business decision-making, digital productivity, knowledge automation, and the emerging AI-driven economy across Africa.
Our Vision
To be a leading African research and technology lab known for creating accessible, high-impact solutions that empower the next generation of innovators, researchers, clinicians, and data-driven decision-makers — not just in Nigeria, but across the continent and beyond.
We measure success not by revenue alone, but by the number of researchers who submitted stronger theses, the patients who received earlier diagnoses, and the policies backed by data rather than guesswork.
Why SABABAT left
Jenni AI, Consensus & scite.ai
behind
Jenni AI, Consensus, and scite.ai are writing and discovery tools. They are excellent at what they do — helping researchers write faster, find papers, and check citations. But they were never designed to think critically about your research design.
SABABAT does something none of them do: it evaluates your methodology. If you propose a research design that is statistically flawed, poorly matched to your objectives, or methodologically weak — SABABAT will reject it and recommend the correct approach before you build an entire project on a broken foundation.
Jenni AI will help you write what you want, build on it, and make it sound convincing. SABABAT will stop you, flag the problem, and give you the methodology your research actually needs — even when that is not what you asked for.
"The first research AI that will tell you no — and explain exactly why."
When a researcher submits a flawed methodology, SABABAT does not silently comply. It identifies the mismatch between the research objectives and the proposed design, explains the statistical or structural reason it will not work, and presents the appropriate alternative. This is not obstruction — it is the difference between a tool that writes for you and an intelligence that thinks with you.
| Capability | SABABAT 4.0 | Jenni AI | Consensus | scite.ai |
|---|---|---|---|---|
| Methodology critique & rejection | ✓ Yes | ✗ No | ✗ No | ✗ No |
| Full Chapter 1–5 writing | ✓ Yes | ~ Partial | ✗ No | ✗ No |
| Statistical analysis (40+ methods) | ✓ Built-in | ✗ None | ✗ None | ✗ None |
| Dissertation proposal writing | ✓ Yes | ~ Limited | ✗ No | ✗ No |
| Essay writing | ✓ Yes | ✓ Yes | ✗ No | ✗ No |
| Hallucination-free citations | ✓ Verified | ~ Partial | ✓ Yes | ✓ Yes |
| Introduction & methodology writing | ✓ Yes | ✓ Yes | ✗ No | ✗ No |
| Summary & insights generation | ✓ Yes | ~ Basic | ~ Limited | ~ Limited |
| Questionnaire / Form Builder | ✓ Yes | ✗ No | ✗ No | ✗ No |
| Data cleaning | ✓ Yes | ✗ No | ✗ No | ✗ No |
| Free core access | ✓ Free | ✗ Subscription | ✗ Subscription | ✗ Subscription |
Latest releases
What we
stand for
Six principles that govern how we build, what we build, and who we build it for.
Human First
Every system we build keeps humans in control. AI assists; it does not replace. Researchers, clinicians, and policymakers must always own the final judgment.
Radical Transparency
No hallucinated citations. No opaque outputs. Every result we generate is traceable, reproducible, and honest — even when honesty is inconvenient.
African Context
We build for African data realities — fragmented records, inconsistent standards, limited infrastructure. Our solutions are designed to work here first.
Ethical by Design
From social safety algorithms to clinical AI, ethics is not an afterthought. It is baked into our architecture, data handling, and team culture.
Accessible Intelligence
Power tools should not cost power-user budgets. SABABAT's core analysis is free. Affordability is a product decision, not a marketing promise.
Reproducibility
Research that cannot be reproduced is not research. All Abbadh Labs systems produce outputs that can be traced, audited, and independently validated.
Work on problems
that actually matter
We are a small, focused team building tools that change how African researchers, clinicians, and institutions work with data. Every person on the team touches real product decisions and real users.
We do not have an HR department. If you want to work here, send a direct email explaining what you would build, fix, or improve. That is the application.