How Cognito Thinks About Building AI

At Cognito Systems, we don’t start with models.

We start with problems.

If you’ve spent any time around AI tools lately, you’ve probably felt it too: the demos are impressive, the promises are big, and then you try to use the product in your actual day, and something quietly breaks.

A workflow doesn’t quite fit.

Context gets lost.

A “smart” system gives a confident answer that’s just wrong.

That gap between what looks good and what actually works is where most AI systems fail. It’s also where we spend almost all of our time.

This is how we think about building AI.

We design the system before the model

Before we write a line of code, we ask a few simple questions:

What problem are we actually solving?

Who is affected when this goes wrong?

What constraints exist in the environment where this system will live?

In African contexts especially, infrastructure, data availability, and user behaviour vary widely. If a system isn’t designed with those realities in mind, even the best model will struggle once it leaves a demo.

So we map the entire system first, inputs, outputs, dependencies, and failure points, before deciding where AI truly belongs. The model comes after the system, not before it.

We don’t force AI where it doesn’t belong

Not every problem needs AI.

Sometimes a rule-based system is more reliable.

Sometimes a hybrid approach works better.

Sometimes intelligence is only needed in one narrow part of a workflow.

We’re deliberate about where AI is applied and where it isn’t. That restraint keeps systems easier to maintain, easier to explain, and far more resilient over time.

Depth, for us, isn’t about complexity. It’s about precision.

We treat context as a requirement, not an edge case

Most AI systems are trained and tested in environments that don’t reflect African realities. Data gaps, language diversity, connectivity issues, and different usage patterns all change how systems behave in practice.

We treat those conditions as first-class design inputs.

That means:

• questioning default datasets,
  

• designing for intermittent infrastructure,
  

• and validating systems in real environments, not just test ones.
  

If a system can’t survive context, it can’t scale. It will only fail later, when the cost is higher.

We prioritise reliability over novelty

We care less about what looks impressive and more about what works consistently.

A slightly less accurate system that is stable, observable, and explainable is often more valuable than a cutting-edge one that breaks under real-world conditions. Especially when real people and real money are involved.

We optimise for:

• reliability,
  

• observability,
  

• and long-term performance.
  

Trust isn’t something an AI system should demand upfront. It’s something it should earn quietly, over time.

For us, building AI isn’t about chasing trends or shipping clever tricks. It’s about designing intelligent systems that work in context, survive constraints, and scale with intention.

That philosophy is what guides everything we’re building right now, including Martha, our AI support system, and it’s what we’ll keep testing, refining, and learning from.

If this way of thinking resonates, you’ll understand what we’re trying to build long before we ever have to explain it.

Cognito Systems