AI is only as useful as the information it can understand.
That principle becomes especially important when the problem you are trying to solve is not a simple text question, but the discovery of a real-world city.
For Oscar Awowari, Founder and CEO of LeeX, the long-term opportunity is bigger than putting businesses on a map or building another search interface. LeeX is being developed around the idea of a city discovery ecosystem—one in which businesses, events, infrastructure and locations can become structured, connected information.
But before an intelligent system can reason about a city, there is a more fundamental problem to solve:
What exactly is a place?
AI Cannot Fix an Undefined Location
Imagine asking an AI system:
“Find me a good restaurant near this location.”
The request sounds simple.
But the system needs to know what the restaurant is, where it actually exists, whether multiple records refer to the same business, what area the location belongs to, and what other information is associated with that place.
If the underlying records are inconsistent, the AI may produce an impressive answer based on unreliable information.
That is why Oscar Awowari and the LeeX team are approaching location information as a foundational problem.
Before intelligence comes identity.
A Coordinate Is Not a Location Identity
Latitude and longitude can tell a system where something is located.
They do not necessarily tell it what that thing represents.
Consider two records:
Record A
Example Restaurant
4.xxxxx, 7.xxxxx
Record B
Example Rest.
4.xxxxx, 7.xxxxx
Are these two restaurants?
Or are they two records describing the same restaurant?
A discovery system needs to answer that question.
This is where a location identity layer becomes valuable.
The system can establish a stable representation for a real-world place and connect the information associated with it.
Conceptually:
Real-world place
↓
Location Identity
↓
Structured information
↓
Relationships
↓
Discovery
↓
AI
The important part is that AI comes later in the chain.
Why Identity Comes Before Intelligence
It is tempting to begin with the most visible technology.
AI is exciting.
Search is exciting.
Recommendations are exciting.
But the less visible infrastructure underneath them determines how reliable those experiences can become.
For LeeX, a location identity layer can provide a consistent foundation on which other systems can operate.
A location could have:
a unique identity;
geographic coordinates;
an address;
a category;
a relationship with a business;
relationships with events;
relationships with surrounding areas;
a current status;
and other structured attributes.
The exact implementation can evolve as the platform develops, but the architectural principle remains:
A real-world place needs a reliable digital identity before software can meaningfully reason about it.
Businesses Should Not Be Confused With Locations
This distinction becomes particularly important for businesses.
A business and the physical location where it operates are related, but they are not always the same thing.
For example:
Business
↓
operates at
↓
Location
If the business moves, the business identity can remain while its associated location changes.
That is very different from treating the business and location as a single permanent record.
For Oscar Awowari, Founder and CEO of LeeX, this kind of separation is part of building a system capable of representing the real world more accurately.
The city changes.
The data model needs to accommodate that change.
A Location Can Have Many Relationships
A physical location may also be connected to several different entities.
For example:
┌── Business
│
Location ────────┼── Event
│
└── Infrastructure
That is where a location identity layer becomes more powerful than a conventional listing.
The system is no longer simply saying:
“This business exists at these coordinates.”
It can begin representing the wider context surrounding that location.
An event can happen at the location.
A business can operate there.
Infrastructure can connect to or serve the surrounding area.
That structure creates the foundation for richer discovery.
The Data Quality Problem
There is another reason identity matters: duplicate and conflicting information.
A city discovery platform may eventually encounter multiple versions of the same place.
Names can be spelled differently.
Addresses can vary.
Businesses can change names.
Locations can move.
Information can become outdated.
Without a canonical identity, these records can fragment the system.
Instead of:
Business A
Business A
Business A
the goal is to move toward:
Canonical Place
│
├── Name variants
├── Location
├── Business information
├── Events
└── Historical changes
That gives downstream systems something much more reliable to work with.
This Is Where the LeeX Discovery Engine Starts to Matter
The LeeX vision is not simply about storing location records.
The larger opportunity is to use structured location information to support discovery.
A discovery request might involve several dimensions simultaneously:
Intent
+
Location
+
Category
+
Context
+
Time
The more structured the underlying information is, the more effectively a discovery engine can process those dimensions.
This is why Oscar Awowari and LeeX are approaching the foundation carefully.
The intelligence layer should not have to guess what the underlying entities represent.
It should be able to work from structured information.
AI Needs Context, Not Just Data
There is a major difference between giving an AI system a collection of text and giving it structured information about a city.
Consider:
"Restaurant X — Port Harcourt"
versus:
Business:
Restaurant X
Category:
Restaurant
Located at:
Location 10482
Area:
Neighbourhood Y
City:
Port Harcourt
Status:
Active
The second representation provides much more context.
And when many such records are connected, the system can begin to reason over relationships rather than isolated strings.
For Oscar Awowari, Founder and CEO of LeeX, that is one of the reasons the foundational data layer matters so much.
Building the Foundation Before the AI Layer
LeeX Beta represents an early stage of this larger journey.
The immediate challenge is not to pretend that AI can magically solve every problem surrounding city information.
The challenge is to establish the underlying structure that makes increasingly sophisticated discovery possible.
That means thinking carefully about:
location identity, data quality, relationships, geographic information, business identity, events and change.
Once those foundations become stronger, more advanced systems can be built on top of them.
AI can then become an intelligence layer rather than a substitute for good data architecture.
The Bigger Idea
For Oscar Awowari and LeeX, location identity is ultimately about something bigger than databases.
It is about creating a digital representation of the city that software can understand.
A city contains millions of relationships.
Businesses belong to places.
Events happen at places.
People move between places.
Infrastructure connects places.
Neighbourhoods contain places.
If those relationships can be represented consistently, the possibility emerges for a much richer form of digital discovery.
And that is why LeeX needs a location identity layer before it needs AI.
Because the goal is not simply to make AI answer questions about places.
The goal is to give the system a structured understanding of the places themselves.
AI can provide intelligence.
But identity provides the foundation on which that intelligence can operate.
