Company
Baobyte Consulting builds the Baobyte AML Suite: an evidence-led anti-money-laundering and counter-terrorist-financing platform for Nigerian regulated institutions — banks, microfinance banks, fintechs and payment service providers, bureaux de change, insurers, capital markets firms and the designated non-financial businesses and professions supervised under the Nigerian regime.
Why it exists
Most compliance teams in Nigerian institutions are not short of alerts. They are short of the ability to prove, months later, why a particular decision was the right one: which rule fired, against which version of which list, what the analyst saw at the time, who approved it, and what was filed. When that evidence has to be assembled by hand from spreadsheets, email threads and a core banking export, an examination becomes an archaeology exercise.
The Baobyte AML Suite is built around that problem rather than around detection volume. Customer due diligence, transaction monitoring, screening, investigation and regulatory reporting sit in one system, and each decision carries the evidence that produced it — the rule version, the list snapshot, the approval, the audit event — so that the answer to “why did you not file this?” is a record rather than a recollection.
It is deliberately not a black box. Monitoring rules are explainable and can be replayed deterministically; risk models are versioned and inventoried; content packs carrying rules, weights and report templates are signed and diffable. An institution that cannot explain its own detection logic to a supervisor has bought a liability, however good the detection is.
How we build
Each of these is a behaviour you can observe in the product rather than a value on a wall. If one of them is not true of a release, that is a defect.
Unsafe configuration stops the process at startup with an explicit message rather than running in a weakened state. A typo in an environment mode fails the boot; it never widens behaviour. A deployment that appears to work while a control is off is the hardest kind of problem to find.
A screening provider that times out returns an explicit unavailable outcome, never a not-a-hit. A metric stream that is down fails closed rather than reporting zero. The most dangerous thing an AML system can do is quietly record an absence of evidence as evidence of absence.
Rules raise alerts; they do not close relationships or file reports. Sensitive decisions run through maker-checker with the actor, the payload digest and the timestamps recorded, and the same person cannot approve their own request. The system's job is to make a human decision defensible, not to replace it.
The platform is packaged to run entirely inside an institution's own infrastructure, including air-gapped. The public web surface issues no cross-origin request at all — no external fonts, scripts, analytics or image hosts — so it keeps working in an installation with no route to the internet.
Reporting, customer due diligence, address and identifier handling and the DNFBP model are designed against Nigerian regulation rather than adapted from a European or American template. Where an authoritative regulator artefact has not been obtained, the product refuses to file against a guessed schema.
The design set carries an explicit register of unresolved questions, and the code carries markers where a compliance determination is still owed. A documented gap is a gap someone can close; a plausible answer written into the product is a gap nobody goes looking for.
People
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Corporate details
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Security controls, subprocessors, certification status and availability — with the gaps stated.
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