Building AI Products in Nigeria: Challenges, Solutions, and Opportunities

A practical guide to building ML solutions in the Nigerian context, covering infrastructure, data, and market opportunities.

Why Nigeria Is a Perfect (and Brutal) Testbed

Nigeria is one of the most interesting places on earth to build AI:

If you can ship a reliable ML product here, you can ship it anywhere.

This post breaks down what it actually takes to build AI products in Nigeria—based on shipping systems like SabiScore to real users.

Challenge 1: Infrastructure Reality (Power, Network, Cloud)

Let’s start with the obvious: nothing is stable by default.

Instead of complaining, design for it.

# Robust HTTP client with retries (essential in Nigerian conditions)
import httpx
import tenacity

@tenacity.retry(
    stop=tenacity.stop_after_attempt(5),
    wait=tenacity.wait_exponential(multiplier=1, min=1, max=30),
    retry=tenacity.retry_if_exception_type(httpx.RequestError),
)
async def safe_get(url: str) -> dict:
    async with httpx.AsyncClient(timeout=8.0) as client:
        response = await client.get(url)
        response.raise_for_status()
        return response.json()

Design rule: Assume every network call fails once. Your systems should retry gracefully, log smartly, and avoid blocking the user.

Challenge 2: Data Is Messy, Late, or Non-Existent

You will not find a clean, well-documented "Nigeria-ML-dataset.zip" for your problem.

For SabiScore, we needed:

What we actually got:

Solution: Build a data aggregation layer.

DATA_SOURCES = ["primary_api", "backup_api", "manual_entry"]

async def fetch_match(match_id: str) -> dict:
    errors = []
    for source in DATA_SOURCES:
        try:
            data = await fetch_from_source(source, match_id)
            if validate(data):
                return data
        except Exception as e:  # log properly in real code
            errors.append((source, str(e)))
            continue
    raise RuntimeError(f"No valid data for {match_id}: {errors}")

Key mindset shift: you’re not just training models—you’re building data products in a hostile environment.

Challenge 3: Talent and Skill Gaps

There are brilliant engineers in Nigeria. But the pipeline from:

"I took a Coursera ML course"

to:

"I can deploy, monitor, and maintain an ML system in production"

is still very narrow.

If you're leading an AI product here, you must:

One simple pattern that worked well for us:

# Simple, readable service boundary between ML code and API

class PredictionService:
    def __init__(self, model_registry, feature_store):
        self.model_registry = model_registry
        self.feature_store = feature_store

    def predict(self, request: "PredictionRequest") -> "PredictionResponse":
        features = self.feature_store.build_features(request)
        model = self.model_registry.get_current_model()
        probs = model.predict_proba([features])[0]
        return self._to_response(request, probs, features)

This separation makes it easier for more junior engineers to touch API code without breaking ML logic.

Massive Opportunity: Local Context, Global Tools

Where Nigeria really shines is context.

You understand:

Combine that with global tooling:

And suddenly you can build products foreign companies can’t:

Practical Opportunities You Can Build Now

Here are a few ideas that sit at the intersection of feasible and high-impact:

  1. "Explain my bet" assistant for sports bettors
  2. Creditworthiness scoring for POS agents and micro-SMEs
  3. Logistics ETA prediction for Lagos delivery routes
  4. "Power outage forecaster" using public data + private signals
  5. Fraud detection for local fintech transaction streams

Each one is:

Lessons From Shipping SabiScore

What I’d tell any Nigerian founder building AI in 2025:

Call to Action

If you're building ML products in Nigeria or Africa and want:

Let’s talk. Reach out here and mention this article.

And if you’re just getting started, read next: From Notebook to Production: Deploying Your First ML Web App.