For business
Platforms, apps and integrations, and AI built for your process rather than configured from a template. No two businesses need the same thing here, so we build the whole system: the data work, the models, the infrastructure they run on, and the software they sit inside. Taken to production, operated afterwards, hosted in Nigeria.
First, the boring part
Before anything else: this is a development shop. If you need a platform, an app, an integration, or the unglamorous plumbing between three systems that refuse to talk, that is the job and we will take it. A great deal of the AI work people ask for turns out to need this first, and we would rather say so than sell you a model that has nothing clean to read. A good deal of it is also getting you off a system you have outgrown, without a big-bang rewrite.
The system the business actually runs on: operations, back office, portals for customers and partners, reporting that finance trusts. Often replacing a spreadsheet that three people maintain by hand and everyone is afraid of.
Customer-facing products from first version to scale, or an existing one taken off the ground it has outgrown. iOS, Android, and web.
Getting your systems to talk: core banking, payments, ERP, CRM, regulators, and whichever vendor API was written in 2011. Unglamorous, and usually the thing blocking everything else.
Extraction, indexing, and retrieval over the documents your business runs on: statements, contracts, claims, KYC files, deal rooms. This is the same engineering as our government records practice.
Built is not the same as used.
A system that officers avoid, or customers abandon halfway through, has failed regardless of how well it was engineered. We design the flows and the interface alongside the build, with the people who will actually use it, and we treat the ugly screens as the important ones: the internal form a registry clerk fills in forty times a day matters more than the landing page.
Move off the old system without a big-bang rewrite.
Most institutions here are not starting from nothing. They are carrying a legacy system that half works, a vendor platform nobody can extend, and data spread across it in inconsistent shapes. We replace these incrementally: stand the new system beside the old one, move one capability at a time, reconcile the data and prove the reconciliation, and keep both running until the old one is genuinely unused. Big-bang rewrites are how these projects fail, and we will argue against one.
Design is on that list deliberately. A system your staff avoid, or your customers abandon halfway through, has failed however well it was engineered, and the internal screens nobody demos are usually the ones that decide it. Full service list.
Then, the AI
Most AI in Nigerian businesses is a chat window bolted to the side of a process that carries on exactly as before. What pays is AI inside the workflow, taking an action, with the reasoning logged.
There is no product here to sell you. What a bank needs from AI and what a logistics operator needs are different systems, and neither is a configuration of the other. The work takes three broad shapes, and yours will be one of them or a combination.
Retrieval over your contracts, policies, statements, or deal room, where every answer cites the page it came from. Your team stops hunting through PDFs and starts checking a citation.
Hours back, and an answer someone can defend in a meeting.
The work that is too big to do by hand and too consequential to guess at: fraud and transaction monitoring, credit decisioning on thin files, claims triage, KYC and document verification. A model proposes, your officer decides, and the log records which.
Loss avoided, decisions in minutes instead of days.
The step most AI projects never reach. The model is not a chat window on the side, it sits inside the process: it opens the case, populates the file, routes the exception, and hands a human the decision that needs one.
Capacity that does not need headcount, and a process that can be audited.
What we actually build
Plenty of firms will put a chat box over an API and call it an AI system. That is one layer of seven, and it is the easiest. When the answers are wrong, nobody can say why; when the model changes underneath, nobody notices; and when the data was never fit to read in the first place, no amount of prompting repairs it. We build the whole stack and operate it afterwards.
Pipelines into the systems that hold your data, extraction from the documents that are not in a system at all, and the cleaning nobody budgets for. Most AI projects fail here, months before a model is ever the problem.
A frontier API where quality justifies it, an open-weight model on hardware you control where the data cannot leave, or both behind a router that decides per request. We are not tied to a vendor, so the choice is made on your constraints instead of our billing relationship.
The model reads your contracts, policies, statements and records rather than its training data, and every answer names the source. This is the layer most products stop at. For us it is the middle of the build.
A test set built from real decisions your team has already made, so quality is measured against your work rather than a public benchmark. Without this, nobody can say whether a change made the system better or worse, and most teams cannot.
Who may ask what, what may leave your perimeter, what is logged and for how long. Prompt and response logging, retention aligned to your policy, and a trail your risk and legal functions can review rather than take on trust.
The queue, the case file, the approval step, the exception path. A model that cannot open a case or route an exception is a demo. This is the software half, and it is usually larger than the AI half.
Monitoring for drift and cost, re-evaluation as your data shifts, model upgrades when something better ships, and the support call in year two. An AI system is not finished at launch; it is a system that has to be operated.
Not every engagement needs all seven. A business with clean data in one system starts at the model; a business whose contracts live in a shared drive starts at the first and that is most of the work. We tell you which one you are before quoting, because getting that wrong is how these projects end up abandoned.
If your data cannot leave
Somebody in your business pasted a customer list, a draft contract, or a board pack into a chat window this week. Blocking the tools does not fix that, it moves it onto personal phones where you have no visibility at all. The workable answer is to give people something at least as useful that runs where you can see it.
Your own AI, over your own data, inside your own walls.
Your team is already using AI. You just cannot see which parts of the business are going into it. Blocking it does not work, it moves the problem onto personal phones where you have no visibility at all. The answer is to give people something at least as useful that runs where you can see it. We deploy open-weight models on infrastructure you control, your own data centre, your private cloud, or in-country colocation, with retrieval over your own documents and every answer citing its source. Nothing leaves your perimeter unless you decide it does.
What we will not oversell
A self-hosted open model is good, not frontier. On the hardest reasoning there is still a real gap against the best commercial models, and self-hosting is cheaper only at volume because the GPU cost is fixed whether you use it or not. Where quality matters more than secrecy we will say so, and route only the work that genuinely cannot leave.
What it pays
Roughly four in five enterprise purchases need finance sign-off, so every proposal we write carries the business case in the language the person signing it uses. Not accuracy percentages. These:
Enterprise buying benchmark, ~79% requiring CFO sign-off. Sources in our internal market file, reviewed May 2026.
The same team handles more volume. This is usually the real prize in Nigeria, where the argument is not that labour is expensive but that the good people are scarce and stretched.
Applications abandoned mid-onboarding, receivables nobody chased, customers who could have been approved and were not.
Caught earlier, at volume, with the reasoning logged so risk and audit can see why.
Days to minutes on the decisions your customers actually feel, and the ones your regulator asks about.
Model governance, an audit trail, and a human in the loop where it matters. So the board and the regulator say yes rather than maybe.
One thing we will not claim: that AI is cheaper than your staff. A Nigerian analyst does not cost what a New York one costs, so “replace people to save money” is a weak and usually false argument here. The honest one is capacity: the same good people, who are scarce, covering far more ground.
Where we start
In four weeks, a costed, prioritised plan to put AI to work, and proof it can.
We audit your data, infrastructure, and compliance exposure, rank the use cases by return, and where feasible build a small working proof on your real data. You leave with a plan your CFO and your risk team can both get behind.
2–4 wks · fixed fee
Or skip it and come straight to us with a build. Plenty of buyers know exactly what they want made, and the sprint is there to de-risk uncertainty rather than to be a toll gate.
Where we go deepest
Financial services is where we are deepest, and where NDPA, CBN expectations and model-risk governance are built into how we work rather than added at the end. Investment banking and capital markets have their own playbook.
Work
KYC onboarding cut from three days to under ten minutes.
A two-week readiness sprint, then a document-processing and identity-verification pipeline, hosted in-country, with a full DPIA and audit trail so the risk team signed off before launch.
READINESS · BUILD & SHIP · COMPLIANT BY DESIGN
Contact
Twenty minutes. We will tell you what we would do, and say so plainly if we are not the right firm for it.