Linguistic Infrastructure for AI

The Tonal Operating Systemfor Bantu Language AI

664 languages. 400 million speakers. One engine. Structured training data with full morphological decomposition, tonal ground truth, and speaker-validated audio.

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Architecture

Three layers. One compound system.

Each layer is valuable alone. Together they form an irreplicable moat.

BTS Engine

Bantu Technical Standard Engine

  • ● 250M+ records, 16 language-agnostic generators (LAGs), 664 POC cartridges
  • ● 9-slot verb template: every valid permutation with full decomposition
  • ● Language-agnostic: swap the cartridge, generate any Bantu language

Amina / Our Words

Human Validation at Scale

  • ● Native speakers record at 48kHz mono across 25+ countries
  • ● Speaker calibration profiles from syllabic ground truth (140+ syllables/language)
  • ● Peer review, gamified contribution, milestone payments
  • ● Every recording validated against the engine's predictions

Tonal Ground Truth

The Missing Dimension

  • ● Standard orthography hides tone — our pipeline reveals it
  • ● 5-step deterministic tonal assignment (Lexical → Meeussen → Melodic → Spreading → OCP)
  • ● Acoustic minimal pairs: same words as statement vs question
  • ● Models learn the rules of tone, not just surface patterns

The Problem We Solve

Current AI is tonally blind.

Standard Bantu orthography does not mark tone. The same written word can mean completely different things depending on pitch. Every AI model trained on this text is reasoning over an impoverished map of the language.

The Flat Text Problem — What the LLM sees vs what the speaker knows
Read the full paper →
Independently Verified

100% tonal accuracy across 132 stress-test records

Reviewed by Gemini 2.5 Flash — Meeussen's 48/48, Spreading 82/82, Nasal Harmony 22/22, Melodic Override 26/26

664
Languages
250M+
Records
48kHz
Audio Quality
140+
Syllables / Lang
5
Validation Dims

What You Get

Structured training data for Bantu language AI

Morphological Training Data
JSONL with full 9-slot decomposition, morpheme labels, provenance
Tonal Verb Pairs
Declarative/interrogative acoustic minimal pairs with F₀ contours
Syllabic Acoustic Baselines
Per-speaker F₀ calibration from isolated syllable recordings
48kHz Mono Audio
Speaker-validated recordings with validation passports
HFST Finite-State Transducers
Compiled morphological analyzers for offline O(n) analysis and generation
Code-Switching, Conversations, Stories
Multi-language recordings and naturalistic speech PLANNED

Team

Built by domain experts

MM

Co-Founder

PhD Chemical Engineering

Neural networks, genetic algorithms, fuzzy logic. Native speaker of Bemba, Nyanja, Tonga.

MM

Co-Founder

PhD Quantum Physics

Zambian/UK. Speaks Bemba, English, fluent German. Learning Japanese & Chinese.

CF

Co-Founder

PhD Professor

Speaks Bemba. Academic rigor and institutional research connections.

Built by 3MegaLabs — the applied AI research arm of 3Mega.ai

Pricing

Access at every scale

Evaluation
Free

1,000 records · 5 languages · Sample clips · 100 req/day

Academic
Free

5,000/day · All languages · Research access · 500 req/day

Startup
Contact

50,000/mo · 10 languages · Standard audio · Full API

Recommended
Enterprise
$1.75M/yr

Unlimited · All 664 · Full 48kHz corpus · MCP integration

See full plan details →

Ready to build AI that truly understands Bantu languages?

Start with the free evaluation tier. No credit card required.

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