What we do
Start where the risk is, and climb.
There are two ways in, because a ministry and a bank are not buying the same thing. Both are short, fixed-fee, and built to retire the biggest unknown before anyone commits to a build. Then design, the build itself, modernisation, and running it afterwards.
Records Readiness ReviewFor government
In a month, an honest inventory of what your registry actually holds, and proof of what it would take to make it answerable.
We inventory one records estate: what exists, in what medium, in whose custody, at what quality, in what volume. Then we test extraction on a real sample of your actual documents, not on assumptions. You leave with a records inventory, a feasibility assessment, a costed roadmap, and a working proof. We will not quote a build without this, because extraction is where these projects die and where vendors under-quote.
AI Readiness SprintFor business
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.
Build & Ship
We take it to production, and own the result.
We take the highest-value use case to production: extraction, indexing, retrieval with citations, security, monitoring, and integration with what you already run. Milestones map to deliverables you can see, and for government work to certificates you can sign.
Run It For You
We operate it after handover, and hand it over properly if you ever want us gone.
We operate the system after handover: monitoring, evaluation, retraining, support. Every engagement also carries an exit package from day one, an export in open formats, the source, a runbook, and a transition period. A client who can leave cheaply stays for the right reason.
Compliant by Design
Your data stays in Nigeria, you own the index, and every answer is auditable.
Government data stays in Nigeria, because NITDA requires it and because it is the right default. Sensitivity classification fails closed. Every answer, routing decision, model version and human review is logged to an append-only trail you can query without asking us. DPIAs, retention aligned to your own schedule, and FOI readiness.
Also, as full engagements
Design, and getting off the old system.
Two things that are not steps on the ladder above, because clients come to us for them directly as often as they arrive inside a larger build.
Product & Interface Design
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.
- User research with the people who do the work, not just the people who commissioned it
- Flows and information architecture before screens
- Interface design, and a design system when there is enough surface to justify one
- Accessibility built in, which for a public body is an obligation rather than a nicety
- Design of the unglamorous internals: forms, queues, exception handling, admin tools
Modernisation & Migration
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.
- Assessment of what the legacy system actually does, including the parts nobody documented
- Incremental replacement, capability by capability, rather than a cutover weekend
- Data migration with reconciliation you can audit, not a one-way import and hope
- Consolidating several systems, or several spreadsheets, into one source of truth
- Upgrades and replatforming of vendor systems already in place
- Decommissioning: proving the old system is unused before switching it off
In-House AI
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.
- A data-classification pass first: what exists, what may leave, and what your people are actually doing with AI today
- Open-weight models deployed on your infrastructure, sized to your real volume rather than to a benchmark
- Retrieval over your own documents, contracts, policies and records, with citations
- A sensitivity router: sensitive work stays inside, and non-sensitive work can still reach a frontier model if you want the quality
- Access control, prompt and response logging, retention and audit, so risk and legal can sign off rather than block
- We operate it: model updates, monitoring, evaluation as your data and questions change
And we bring
Capabilities inside every engagement.
Real, and available standalone where it makes sense, but usually part of a larger piece of work rather than the reason a client calls.
Cloud, DevOps & platform engineering
CI/CD, infrastructure as code, monitoring and alerting, cost control, and in-country hosting where residency requires it. Every build we ship carries this; we will also do it against a system somebody else built.
Security review
Application and infrastructure review against the way these systems actually get broken into. Paired with the data-protection work rather than sold as a compliance tick.
Technical due diligence
For investors and acquirers: an honest read on a target’s codebase, architecture, team and key-person risk, and what it would cost to fix. Short, senior, and written to be read by people who are not engineers.
Fractional technical leadership
Where an organisation needs senior technical judgement in the room, on architecture decisions, vendor selection, or a hiring plan, without a full-time hire yet.
Training & capability transfer
Deliberately part of every engagement rather than an upsell: your team works alongside ours and can operate the thing when we leave. For a public body this is close to the point, since a system nobody inside can run is a system that gets abandoned.
Geospatial & mapping
Land, planning and permit records are inherently spatial, and we work with them as such: linking records to parcels, and building on established GIS platforms. A full GIS platform build we would take on with a specialist partner rather than alone, and we would say so up front.
What we do not do: rent you engineers by the head. We price and plan around your result, so staff augmentation is not on the list and will not be. We also do not take on blockchain or embedded work, because we have no credible capability there and would rather say so.
Pricing
What it costs.
We price to value and scope to each engagement, so the number comes out of a discovery call rather than a rate card. These are the floors, not the range.
Government
We quote in naira. No gain-share, no contingency, no revenue share, and we never take custody of a collection account. That is a red line, not a negotiating position.
Private sector
A four-week AI Readiness Sprint starts at $5,000. Production builds start at $75,000. Managed retainers start at $3,000 a month.
Where it pays off
Public sector
- Records survey and inventory
- Registry extraction and digitisation
- Revenue-base enumeration
- Backlog triage and ageing
- Appropriation and spend analysis
- Internal copilots over policy and circulars
Financial institutions
- Fraud detection
- KYC and onboarding
- Credit scoring
- Document processing
- Model governance
Enterprise
- Internal copilots
- Knowledge retrieval
- Process automation
- Demand forecasting
Contact
Let's talk about what you are building.
Twenty minutes. We will tell you what we would do, and say so plainly if we are not the right firm for it.