Cuespaces was built after going through infrastructure site evaluation the hard way — once on instinct, once properly. This is what we learned, and what we built so you don't have to learn it the same way.
The question was simple: where in Port Harcourt should we build? Gas is one of the few commodities where a customer in a high-brow area and a customer in a working-class neighbourhood pay roughly the same price. So density and economic profile mattered, but not in the way most retail businesses care about them.
Two things mattered most: road infrastructure, because tanker trucks on bad roads don't just cause delays — they cause accidents, and a gas accident means explosion, liability, and a community relationship you can't rebuild. And expansion potential, because the original plan was always to scale, and a site that works at 20 metric tonnes needs to have a path to 1,000.
We walked sites. We asked around. We relied on a network, on instinct, and on local knowledge built over years. Eventually, we found a site that worked.
We got lucky. We just didn't realise how lucky until the second time.
The data centre evaluation started with AI. Not because it was fashionable — because the variables were too complex to navigate manually. Power line proximity. Gas pipeline access for when the grid can't carry the load. Fibre infrastructure. Existing demand from neighbouring businesses. Expansion headroom at Phase 1 and at 40MW. Community relationships. Title clarity.
We used AI research tools, GIS layers, open infrastructure maps, and news archives. We cross-referenced findings. We validated claims. We found a 33kV feeder within connection distance, a Shell pipeline accessible because the land sits on the East-West Road — a highway cutting across seven states — and evidence of a neighbouring business already connected to the same feeder.
The land happened to be owned by the same landlord we'd worked with for the refilling plant. But that wasn't why we chose it. We chose it because the research pointed there.
The research took weeks, not months. And it worked because we had built the process.
The scorecard is built from what your project needs to succeed — not from what data we can easily find. Scale, cost constraints, and expansion plans drive the attribute set.
Location intelligence that pushes to the most granular data available — named feeders, pipeline routes, specific corridor characteristics — not country-level generalisations.
Scores that say what was confirmed, what was estimated, and what still needs to be verified. A calibrated score you can trust is worth more than a confident score you can't.
The pipeline runs automatically — from scorecard generation through submission, scoring, and reporting. Human attention reserved for decisions, not data collection.
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