SABABAT 4.0 automates the full quantitative research lifecycle — from questionnaire design and literature synthesis to advanced statistical analysis and publication-ready reporting. No coding. No steep learning. Just insight.
Trusted by 1,700+ researchers across Africa
Founded in 2021 by brothers Adenle Muhsin Modupeoluwa and Adenle Fawaz Ifedolapo, Abbadh Labs is a bootstrapped Nigerian research-technology company committed to transforming Africa's fragmented data into clear, predictive, and actionable insights.
We are dismantling the "AI for Research" fear by providing researchers with SABABAT, the world's most autonomous quantitative research & statistical tool. By handling every stage of the quantitative research process with a hallucination-free Engine and 40+ advanced statistical analysis, we made research and analysis very easier and accessible. Our mission is to make research and advanced statistical analysis accessible to everyone including non technical users.
We apply data intelligence to healthcare challenges with emphasis on ethics and patient-centred outcomes. Working with Federal Medical institutions, our mission is to integrate an intelligence layer directly into established medical record environments, utilizing Longitudinal Analysis and Time-Series Forecasting to transform static data into a proactive shield. By identifying subtle shifts in a patient's historical trends, our engine predicts physiological declines and potential onset of illness before symptoms manifest.
We go beyond simple analytics to build a Proactive Life Infrastructure. By leveraging bio-signal pattern recognition and environmental data, our model GenieStat for motion sickness prediction detects physiological discomfort before symptoms occur. We apply this same predictive DNA to social safety, developing "Red-Flag" algorithms that identify patterns of harassment and systemic corruption before they escalate into physical harm.
To turn Africa into a data powerhouse, we are establishing the continent's first Unified Data Lake for high-stakes benchmarking. We ingest and normalize messy, large-scale datasets from agriculture, energy, and education to provide "National Pulse" reports. This allows governments and organizations to move past guesswork and base their policies on hard, real-time evidence, setting a new standard for accountability across the African continent.
"There is so much to build."
— Adenle Muhsin, CEO
Generate complete, well-structured Chapters 1–5 with real APA 7th edition citations sourced from verified academic databases.
Try NowConceptual, theoretical, and empirical synthesis with auto-citations from 2019–2025. 15–20 page dissertation-grade output.
Try ItValidated, research-focused questionnaire design with Likert scales, demographic blocks, and auto-structured sections.
Build Now40+ methods — T-Test, Chi-Square, ANOVA, Regression, Correlation — with AI-driven deep interpretation in plain language.
AnalyseUpload messy datasets and receive clean, analysis-ready data with outlier detection, normalisation, and structured export.
Get StartedEnter your topic, aims, and objectives. SABABAT guides you step-by-step through your entire research project.
View PricingSABABAT scores highest in accessibility and analysis breadth — matching the analytical power of SPSS and R while maintaining the ease-of-use students expect from Excel, all at a fraction of the cost.
Unlike traditional tools that require licensing fees, coding expertise, or institutional access, SABABAT delivers reproducible statistical analysis, AI interpretation, and formatted academic output from a single browser session.
See Pricing →Agentic models that support decision-making across education, healthcare, and social systems with transparency and reproducibility.
From longitudinal modelling to automated reporting, we bridge the gap between academic theory and deployable application.
Clinical research frameworks and AI-assisted decision systems that improve care delivery with patient-centred, interpretable outcomes.
Scalable analytical systems that validate, process, and render complex datasets into reproducible insights for high-stakes decisions.
SABABAT launched its beta version for early testers and initial cohort of academic users across Nigerian universities.
SABABAT introduced a structured Pilot Study feature allowing researchers to test datasets and validate instruments before full deployment.
The Form Builder was recognised as the most-used feature among nursing students — praised for auto-structured Likert scales and validated questionnaire design.
A smart virtual assistant was integrated across all analysis modules to guide users in interpreting results and writing up their findings.
Abbadh Labs published research on how SABABAT 4.0 reduces student research costs by over 40% compared to traditional outsourcing methods.
Following positive beta feedback, SABABAT moved into public alpha — expanding access to all statistical analysis tools at no cost.
"I had written Chapter 1 to Chapter 3 when my lecturer rejected my literature review. With the deadline so close, I turned to SABABAT, and it completely saved me. The tool gave me exactly what I needed, quickly and professionally. SABABAT is truly a lifesaver."
"SABABAT saved me so much time. The outputs were clean and reliable. I double-checked the references, and they were real and accurate."
"I wish I saw this before I wrote my project, but this is fire anyway."
"Affordable and professional. It feels like a dedicated research assistant that understands academic standards."