EMMAT GOLD.

AI & Data Consulting · Public Health · Policy

Instruments for reading what public systems miss.

Emmat Gold Enterprises is an AI and data science consultancy for organizations, NGOs, and government agencies working on public health, policy, and data problems that need explainable answers. We build calibrated systems for places where the infrastructure to trust a black box doesn't exist yet: pediatric screening, environmental compliance, disease outbreaks.

01 / ABOUT

Calibrated in the field, before the lab.

Research habits, applied to machine learning.

Emmat Gold Enterprises didn't start in tech. It grew out of trying to understand how people, systems, and institutions actually behave, not just how they're assumed to behave on paper.

That background shapes how we build. We don't just ask whether a model works — we ask who it works for, who it might quietly fail, and whether the person on the receiving end of a prediction can actually understand and act on it. In Nigeria, where specialist capacity is scarce and trust in new technology has to be earned, that distinction is the whole job.

We work at the intersection of data science, public health, and policy, partnering with organizations, NGOs, and government agencies who need systems that are accurate and explainable — not one or the other. And our approach carries that same rigor into every engagement.

Calibration note VERIFIED

"A model that's 95% accurate but can't tell a parent, a regulator, or a clinician why — isn't finished. Explainability isn't a feature we add at the end. It's the starting question."

02 / SERVICES

Engagements, dialed to your stage.

From a single diagnostic to ongoing advisory.

01
Diagnostic

Data & AI readiness assessment

A focused review of your data, systems, and use case to determine whether — and how — machine learning can responsibly solve the problem in front of you, before any code gets written.

02
Core delivery

Model design & explainable systems

End-to-end build of predictive models and decision-support tools — from data pipeline to a working, interpretable system your team and your users can actually trust.

03
Retainer

Ongoing advisory & monitoring

Continued oversight as your model meets the real world — performance monitoring, fairness checks, and hands-on guidance as your organization's data science needs evolve.

03 / PROJECTS

Instruments in the field.

Systems we've built and delivered, not demos.

Instrument 01 · NALR

Neuro-Adaptive Learning Recommender

In clinical validation — UCH Ibadan

An early-intervention platform for toddlers with Autism Spectrum Disorder and speech delays, built for markets where pediatric neurologists are scarce and waitlists cost families the critical early window for neuroplasticity.

A parent answers a short behavioral screening (Q-CHAT); an XGBoost model predicts ASD traits, and an NLP recommendation engine matches the child's specific developmental gaps to curated learning apps. An integrated LLM agent then translates the clinical output into a personalized, empathetic plan a parent can start using at home immediately.

Now in active clinical validation with University College Hospital (UCH), Ibadan, toward a formal case study of real-world screening outcomes. The team has grown to include a dedicated ML engineer and a consultant paediatric neurologist.

88.2% accuracy 92.6% recall 86.2% precision
XGBoostNLPLLM agent
Instrument 02 · AtmosAI

Industrial Emissions Compliance Monitor

Built for NESREA

A monitoring system built to help Nigeria's environmental regulator, NESREA, track industrial emissions compliance more reliably — with a specific focus on correcting for bias in the underlying data so enforcement decisions hold up to scrutiny.

Uses SHAP-based bias correction to keep the model's reasoning auditable, not just accurate — critical for a system whose outputs can inform regulatory action.

SHAPBias correction
Instrument 03 · National Outbreak Prediction

Lassa Fever Forecasting System

Nationwide coverage

A national-scale prediction system for Lassa fever outbreaks, covering all 36 Nigerian states plus the FCT — built to give public health responders earlier warning than case-report data alone can offer.

Merges datasets from the Nigeria Centre for Disease Control (NCDC), ERA5 climate reanalysis data, and the National Bureau of Statistics (NBS) to model outbreak risk against both epidemiological and environmental signals.

NCDCERA5NBS
04 / CONTACT

Let's talk about your data.

Open to consulting, collaboration, and partnership conversations.

Working on a public health, policy, or data problem that needs an explainable answer?

Whether it's a diagnostic review, a full model build, or an ongoing advisory relationship — we'd like to hear what you're working on.

Email us →