AI & Machine Learning Company in Lagos

Lagos ML budgets track the fraud and identity surface you are modelling: a scoped AI proof of concept at Lagos rates, a custom production ML model (Flutterwave or Paystack-rail fraud scoring, BVN/NIN identity verification, Kuda or Carbon credit decisioning, Moniepoint POS anomaly detection), or full production AI systems with RAG, multiple models, fine-tuning, and CBN-grade integrations. Scope drivers include an NDPA audit, a baseline model, a hosted demo on AWS af-south-1, MLOps, monitoring, and an NDPA-aligned model card. Lagos blended rates sit below Cape Town and well below Toronto because of University of Lagos, Covenant, and the broader Andela-trained engineering talent pool. We price in NGN or USD with fixed-fee proposals to hedge the naira.

The Nigeria Data Protection Act (NDPA) 2023, enforced by the Nigeria Data Protection Commission (NDPC), is Nigeria’s primary data protection law, building on the legacy NDPR 2019. Section 37 specifically governs automated decision-making, including profiling that produces legal or similarly significant effects. Section 41 governs cross-border transfers (relevant because Nigeria has no in-country tier-one hyperscaler region, so AI workloads default to af-south-1 or European regions). Section 28 requires a Data Protection Impact Assessment for high-risk processing, which Paystack and Flutterwave-rail fraud, BVN/NIN identity verification, and biometric workloads typically trigger. Our discovery phase runs a Section 37 and Section 28 review on your use case, and high-impact builds ship with a model card, bias audit, automated decision-making notice, and the Section 41 cross-border documentation any af-south-1-hosted system requires. We coordinate with your Data Protection Officer (mandatory under NDPA Section 32 for many data controllers) for NDPC compliance audits.

Yes. We ship fintech-rail AI including Flutterwave and Paystack transaction-pattern anomaly detection, Interswitch Verve and Quickteller risk scoring, NIBSS NIP rail AML monitoring, BVN (Bank Verification Number) and NIN (National Identification Number) identity verification pipelines, USSD-channel fraud scoring (USSD remains the dominant low-bandwidth fintech channel in Nigeria), and Daraja-style API-integrated risk scoring on Moniepoint, OPay, and PalmPay POS networks. For CBN-regulated banks (GTBank, Zenith, Access, UBA, First Bank, Stanbic IBTC) we apply the CBN Risk-Based Cybersecurity Framework, the Banking and Other Financial Institutions Act, and the CBN Open Banking Guidelines, with challenger model documentation CBN and internal audit need. We have patterned deployments after the public Paystack engineering blog and Flutterwave’s pan-African payment architecture.

Nigeria operates in English (official) plus Hausa (roughly 70M+ speakers, dominant in northern Nigeria), Yoruba (roughly 45M+ speakers, dominant in the south-west including Lagos), and Igbo (roughly 30M+ speakers, dominant in the south-east). Pidgin English is the de facto lingua franca across class and region. We build multilingual NLP pipelines using AfroLM, Lelapa AI’s InkubaLM, Masakhane NLP corpora, and HausaBERT, YoruBERT, and IgboBERT where available, supplemented with manually curated Pidgin datasets for informal commerce and USSD chat. For voice workloads we evaluate Google Cloud Speech-to-Text against locally trained acoustic models because hyperscaler defaults under-perform on Nigerian English accents and code-switching. NDPC notices and customer-facing automated decision disclosures are produced in English plus the relevant regional language, not English-only as an afterthought.

USSD remains the dominant fintech and telco channel for Nigerian Tier-2 and Tier-3 markets, where Tecno, Infinix, and itel feature phones and entry-level smartphones dominate the device mix, and 2G/3G coverage outweighs LTE in many states. AI for USSD has hard constraints: stateless or near-stateless session models, sub-100ms inference budgets at the application server, response payloads that fit USSD’s 182-character limit, and decisioning logic that produces a single integer or short string rather than a free-text generation. We build USSD-aware fraud scoring, BVN/NIN verification, micro-credit decisioning, and bill-pay risk models that run inside the carrier USSD gateway timeout (typically 30 seconds end-to-end), with Africa’s Talking, Infobip, Termii, or direct MTN, Airtel, Globacom, and 9mobile gateway integration. Hosted frontier LLMs are generally unsuitable for USSD; classical ML and small fine-tuned models are.

When a project requires genuine research (Hausa, Yoruba, or Igbo low-resource NLP, USSD-aware AI, federated learning for cross-bank fraud, smallholder-farmer computer vision), we scope collaborations with the University of Lagos, Covenant University, Obafemi Awolowo University, the Lagos Business School, and Data Science Nigeria (the AI Saturdays Lagos community) rather than overselling in-house capability. Our core team handles applied engineering, MLOps, and productionisation, which is where most Lagos AI projects actually stall. For standard work (Paystack-rail fraud, BVN/NIN verification, credit scoring, RAG, fine-tuning, classical ML, computer vision on known architectures) no academic partner is needed. We will tell you up front which bucket your problem fits into, and we have introduced Lagos clients to the Deep Learning IndabaX Nigeria network when industry sponsorship fits their roadmap.

Nigeria has no in-country tier-one hyperscaler region today. We default to AWS af-south-1 (Cape Town) as the closest hyperscaler region at roughly 70 to 100ms RTT from Lagos, with Azure South Africa North (Johannesburg) and Azure West Europe (Amsterdam, often the lowest-latency European option via the WACS and MainOne submarine cables) as secondary options. Cross-border to af-south-1 or Europe is documented under NDPA Section 41 and the NDPC General Application and Implementation Directive. For workloads that must remain in-country we deploy to MainOne (Equinix), Rack Centre, Open Access Data Centres, or Galaxy Backbone Tier III facilities in Lagos and Abuja, accepting the latency and operational cost trade-off versus af-south-1. We document residency in the model card and the data processing agreement so NDPC and CBN auditors have a clear answer.

A typical Lagos fintech model (Paystack or Flutterwave-rail fraud scoring, BVN/NIN identity verification, Kuda or Carbon credit decisioning, Moniepoint POS anomaly detection, AML transaction monitoring on NIP rail) takes fourteen to twenty-two weeks from kickoff to production, assuming clean historical data and CBN Risk-Based Cybersecurity Framework documentation as part of scope. Week 1 to 4 is data audit and NDPA Section 37 baseline. Week 5 to 12 is modelling, iteration, and challenger testing. Week 13 to 18 is MLOps on af-south-1, monitoring, shadow mode deployment, and internal audit review. Week 19 onward is gradual rollout under CBN oversight. If you are pre-data or need labelling for Hausa, Yoruba, or Igbo corpora, add six to eight weeks. We have shipped to this cadence inside Nigerian CBN-regulated stacks.