Seven ways we help you prove your AI systems are governed

Before the full Conformity Report, a faster, direct starting point: a structured consultation surfacing exactly where AI is genuinely in use across the business, who's accountable for it, and where the real exposure actually sits. Delivered as a working AI systems inventory, use-case-level risk findings, named accountability gaps, an ISO/IEC 42001 alignment check, and a prioritised roadmap ordered by real risk, distilled into a summary built for the board, not just the technical team.

A structured assessment scoring your AI system against multiple regulatory and standards frameworks simultaneously: EU AI Act, NIST AI RMF, NIST AI 600-1, and ISO/IEC 42001, with Nigeria's own emerging framework mapped in directly as it finalises.

Built on Afrispan's own twenty-two-obligation Conformity Report methodology. Untested obligations are marked as such explicitly, never silently omitted, the same evidentiary honesty a real audit demands.

A structured, documented assessment of your AI system's impact on the people it affects, directly aligned with the annual impact assessment expected under Nigeria's coming AI legislation.

Generated from a real audit trail wherever automated evidence exists, with every field tagged as automated, attested by direct client input, or explicitly missing, never filled with a plausible guess.

For clients operating or expanding across West Africa, an honest assessment of whether compliance evidence actually transfers between Nigeria, Ghana, and the wider ECOWAS market, rather than an assumption that it does.

Given the African Continental Free Trade Area's active push toward regional MSME trade integration, this is a direct, near-term need for Nigerian enterprises expanding into Ghana and the wider ECOWAS market.

Technical evaluation of an AI system's actual behaviour. Cross-model judging avoids the self-audit blind spot our own testing has measured directly: a same-family AI judge evaluating identical output showed a 0.15 point quality inflation and a 0.23 point gap in catching real errors, compared to an independent, cross-model evaluator.

Adversarial red-teaming is mapped to the OWASP LLM and Agentic Top 10 and MITRE ATLAS taxonomies, with drift monitoring to catch a system quietly degrading over time, not only when it fails outright.

Stated honestly: the evaluation architecture is complete and pytest-verified, but most notebooks run in simulated mode pending funded API billing. This is stated plainly wherever relevant, never implied otherwise.

The service line that distinguishes Afrispan most sharply from a traditional audit or consulting practice: live infrastructure that makes governance operational, not a policy document describing what a client should build themselves.

Automated routing sends AI decisions to confident-pass, confident-fail, or human-review queues. A kill-switch halts automated action immediately and independently the moment a serious finding occurs. A resume mechanism requires a named, accountable person to record a substantive decision, not a single approval click, before automated operation continues.

This is the first phase of Afrispan's technical portfolio to reach a real milestone: built, tested, and verified on real local hardware, not simulated.

For clients whose need extends beyond assessment into building the AI-powered automation itself, sales, customer support, lead management, and internal operations, Afrispan designs and implements it with the same governance discipline already proven in its own live infrastructure, the same kill-switch enforcement and human-oversight routing demonstrated in Afrispan's own Project 3 build, applied to a client's specific automation, built in from the start, not retrofitted after deployment.

This builds directly on the orchestration technology proven in Service Line 6, and positions Afrispan to serve a client across their full AI adoption journey, not only at the assurance layer.