African Language Voice AI

Voice AI built for the complexity of African languages

Starting in Nigeria, ALVA CORE is building voice AI for African languages. Our first deployment is in banking and telecom customer service, built on real Nigerian speech across Pidgin, regional English, and code-switching.

Nigerian Speech, built from the ground up
Banking & Telecom first verticals
Pidgin + Nigerian English Year 1
Built in Nigeria, for Africa

Africa has over 2,000 languages. The world's voice AI barely speaks any of them.

We believe every African deserves to be understood in their own voice. Not a translated version. Not a simplified version. Their actual voice, with all its regional texture, its code-switching, its emotion, and its identity.

ALVA CORE starts in Nigeria because it is the continent's most linguistically complex market and its largest economy. We are building with Nigerian English and Pidgin first, then Hausa, Yoruba, and Igbo, before expanding across the continent. The architecture, annotation methodology, and dialogue framework are built to carry any African language.

Every language we add gets the same rigour: real data collection, native speaker validation across dialects and regions, and deep cultural context at every layer. We do not adapt foreign models. We build from the ground up.

One platform. Every language. The voice of a connected Africa.

Global ASR was never built for this

Commercial speech recognition is trained on Standard American and British English. It fails on Nigerian English, breaks on Pidgin, and collapses entirely when speakers code-switch mid-sentence, which is how most Nigerians actually talk.

ASR Failure at Scale

Leading global ASR models perform poorly on Nigerian Pidgin and Nigerian English. For regional accents and code-switching, accuracy drops further, making them unusable for high-stakes banking and telecom interactions.

Code-Switching Blindness

Nigerian speakers shift between English, Pidgin, and indigenous languages within a single sentence. Global ASR treats each as a separate stream. It cannot follow the switch, and misclassifies intent.

Customer Abandonment

Long hold times on bank and telco lines. Automated systems speak only Standard English, excluding the majority of Nigerian customers from self-service options.

High-Stakes Errors

Agents mishandle a significant share of intents on fraud alerts, transaction reversals, and account security, carrying real financial and regulatory cost for customers and institutions alike.

Live Comparison · Lagos Customer Call
Global ASR
"Abeg I wan check my account, the money wey my oga send yesterday — e never land. Make you check am for me, I dey use GTB."
Intent: UNKNOWN
Action: Escalate to queue
ALVA CORE
"Abeg I wan check my account, the money wey my oga send yesterday — e never land. Make you check am for me, I dey use GTB."
Intent: CHECK_TRANSFER_STATUS
Emotion: Frustrated
Action: Auto-resolve → confirm

Six capabilities, one voice agent

ALVA CORE is purpose-built for Nigerian customer service, from the acoustic model to the dialogue engine to the voice output.

Nigerian Speech Recognition

ASR built to handle Nigerian English, Pidgin, and intra-sentence code-switching. Trained on real Nigerian speech, not synthetic data or adapted global models.

Intent Classification

Classifies banking and telecom intents including check balance, failed transfer, report fraud, buy airtime, and block card, across Nigerian English and Pidgin language varieties.

Emotion & Fraud Detection

Real-time detection of caller distress, anger, and potential fraud indicators. Routes high-risk interactions for immediate human review.

Nigerian Voice Output

Responds in natural Nigerian voice that is warm, clear, and culturally authentic. The customer hears a voice they trust, not a robotic American accent.

NDPR Compliant WhatsApp Voice Native IVR Compatible On-Premise Deployment
Year 1: Lagos Pidgin · Warri Pidgin · Delta English · Calabar English · Edo-inflected English · FCT Nigerian English · Nigerian English
Year 2: Hausa · Yoruba · Igbo

Built by people who know the problem

A focused team with deep domain expertise in Nigerian linguistics, speech AI, and production engineering.

Founder & CEO

Leads product vision, commercial strategy, and pilot partner relationships. Sources proprietary data partnerships and negotiates licensing agreements across broadcasting, telecom, and financial services.

Director of Language Operations

Designs annotation frameworks, speaker metadata schemas, and linguistic quality standards across all Nigerian language varieties.

Head of Machine Learning

Builds and fine-tunes ASR, intent classification, and emotion detection models on Nigerian speech data.

Head of Engineering

Owns cloud infrastructure, dialogue manager architecture, API integrations, and production deployment pipeline.

Annotation Team

We recruit native speakers by dialect and region to annotate, transcribe, and validate training data. Every language variety is verified by speakers from that region, ensuring phonological and semantic accuracy across the full corpus.

Privacy and Responsibility by Design

As we build voice technology for high-stakes financial interactions, we take data privacy seriously. Security and compliance are not afterthoughts. They are built into every layer of the platform.

NDPR Compliant

Full compliance with Nigeria's Data Protection Regulation. Customer voice data is processed, stored, and handled according to national data protection standards.

Data Anonymisation

We work closely with our partners to ensure all data is anonymised before it enters our training pipeline. No personally identifiable or sensitive information is retained. If data does not meet NDPR standards, it does not enter our corpus.

On-Premise Option

For partners with strict data residency requirements, ALVA CORE can be deployed on-premise within the organisation's own infrastructure.

Let's talk.

We are looking for pilot partners in banking and telecom, strategic advisors, and investors who believe in African language technology.

hello@alvacoreai.com  →