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Our Team

Architects of Intelligence

Building the future of African language AI, one dataset at a time.

Jeffrey Inyang

Jeffrey Inyang

Co-Founder & CEO

Jeffrey defines and executes our mission to integrate African languages into the global AI ecosystem, setting enterprise-wide strategy, leading product innovation, and securing the strategic partnerships that turn this vision into measurable impact.

Solomon Eze

Solomon Eze

Co-Founder & CTO

Solomon sets the technical vision that powers our AI validation and data infrastructure. As co-founder, he architects scalable systems, defines engineering standards, and ensures production-ready, globally trusted foundations capable of supporting reliable AI systems at scale.

David Ndubuisi

David Ndubuisi

Co-Founder & CMO

David shapes market strategy and commercial growth, positioning African language intelligence for global adoption. As co-founder, he leads go-to-market execution, strategic partnerships, and revenue strategy, ensuring innovation translates into influence, adoption, and sustainable scale worldwide.

What is Bytte

Bytte is a premium data infrastructure company that builds, cleans, and licenses scarce, high-quality African language datasets for AI and foundation model teams.

We specialize in native-speaker–led speech and text data (ASR, TTS, NLP), rigorously annotated with transparent quality metrics and fully owned, licensable IP.

Bytte enables global AI companies to train better multilingual models using data that is otherwise difficult to source, validate, and scale, while maintaining exclusivity and reliability for high-value applications.

Mission

Help build better AI by providing accurate African language data created by native speakers, so models work better for the people who actually use those languages.

Founding Year

2026

Description of Product/Service

Bytte provides production-grade African language datasets that drive measurable improvements in AI models and are fully licensable for research and commercial use.

Core Solution

Bytte builds high-quality, native-speaker annotated African language speech and text datasets designed specifically for training, fine-tuning, and evaluating modern AI systems.

Quality over volume
Native-speaker–led annotation
Clear IP ownership
Measured model impact

Dataset Types

Speech Datasets

Optimized for ASR and TTS systems

Features:

  • Spontaneous and read speech
  • Multiple accents, dialects, and regions
  • Code-switching with English and local languages
  • Demographic metadata (age range, gender, region)
  • Noise conditions (clean, real-world environments)

Use cases:

  • Speech recognition (ASR)
  • Voice assistants
  • Call-center automation
  • Speech-to-speech and multimodal models

Text Datasets

Designed for language understanding and evaluation

Features:

  • Conversational and instructional text
  • Sentiment and intent-labeled data
  • Code-switched and informal language
  • Domain-specific text (finance, health, customer support)

Use cases:

  • Chatbots and assistants
  • Evaluation and benchmarking
  • Fine-tuning LLMs for local relevance