AI doesn't speakmost of the world.نحن نُصلح ذلك. · Tunasuluhisha hilo.

LocaleNLP is building the foundational language infrastructure for 2.6B people whose languages AI systems cannot understand. Offline-first. Low-resource optimized. Grounded in community data.

50+
Languages Supported
2.6B
People Underrepresented
98%
Offline Capable
<4ms
Edge Inference Latency

Backed & supported by

NVIDIAINCEPTION PARTNER
AWSSTARTUPS
MicrosoftFOR STARTUPS
MetaLLAMA ECOSYSTEM
UNICEFSTARTUP LAB
CUHKRESEARCH PARTNER
Corpus Tokens Processed
0
tokens · validated by native speakers
INGESTION: ACTIVE
Validation Pipeline
Raw Capture
Native Speaker Audit
Clean Tokenization
Human-in-the-loop at every stage
Bias Mitigation
Geographic diversity38 countries
Speaker balanceM/F 51/49
Dialect coverage120+ dialects
ISO 639-3 compliant · Open weights available
[ Industry Impact ]

Language infrastructure that transforms industries.

Where standard AI fails, LocaleNLP delivers — in the fields, classrooms, and clinics of the Global South.

01 / AGRICULTURE
Agriculture · VERIFIED

Voice chatbots for crop and market data.

Smallholder farmers receive real-time crop pricing, weather alerts, and soil advisory in their own language — no literacy or data connection required. Deployed via SMS-linked IVR and offline voice models.

2.4B
smallholder farmers
68%
without smartphones
yield improvement
02 / EDUCATION
Education · VERIFIED

Personalized learning via AI translation.

Curriculum delivered in a child's mother tongue from day one — bridging the gap between national official languages and the 2,000+ languages spoken at home across sub-Saharan Africa and the Arab world.

600M+
out-of-school children
54%
taught in 2nd language
1.8×
comprehension gain
03 / HEALTHCARE
Healthcare · EMERGING

Remote diagnosis via local voice-to-text.

Community health workers in remote clinics use voice-driven diagnostic protocols, symptom triage, and patient record systems — all in the local language, all offline-capable on a basic Android device.

4.5B
lack basic healthcare
1:50K
doctor-to-patient ratio
91%
ASR accuracy (Hausa)
[ Developer API ]

One SDK. Fifty languages. Zero cloud dependency.

Initialize the LocaleNLP SDK and get production-grade African and Arabic language inference in minutes — on-device, offline-capable, deployed to your edge node.

Languages covered50+
Edge latency< 4ms
Offline capableYes — INT4
SDK languagesPython · Node · REST
~/localenlp/sdk/preflight.sh
INITIALIZING
Processed in 312ms · 4 Sources
|Language Intelligence

Live intelligence. Every language.

Real-time processing across 50+ low-resource languages — coverage, entity extraction, ASR confidence, and sub-4ms latency, all verifiable.

GeoLang · Coverage
01HAU
87%
02AMH
74%
03ARB
91%
04YOR
68%
ASR · Confidence
94.2%
WER · Hausa STT
01Hausa STT94.2%
02Amharic STT89.7%
03Darija STT91.4%
NER · Entity Extraction
Me kɔ Accra wo de Ama Owusu
01AccraLOC
02Ama OwusuPER
03Bank of GhanaORG
04TwiLANG
Translation · Latency
4.0ms
Edge inference total
01Tokenize0.8ms
02Embed1.4ms
03Decode1.2ms
04Output0.6ms
Sources · Verified
01ORAOX Crowdsource210k
02CommonVoice AF87k
03Community Scripts14k
04Partner Corpus
LIVE · data refreshes every 30s
View model catalog →
The Gap Is Structural

Why existing AI doesn't scale to the world

LLMs TodayBASELINE
LocaleNLPINFRASTRUCTURE
Training data
95%+ English / European
Training data
Native community datasets
Deployment
Cloud-dependent
Deployment
Offline-first, edge-ready
Hardware req.
High-compute GPUs
Hardware req.
Low-power, on-device
Language support
~100 high-resource only
Language support
50+ low-resource languages
Community trust
Black-box outputs
Community trust
Native speaker validated
Offline access
None
Offline access
Full capability
Infrastructure Layer

A new layer of AI infrastructure

Six interlocking systems that make AI accessible where it was previously impossible.

Low-Resource Language Models

Purpose-built transformer architectures trained on under-represented African and Arabic languages with native speaker validation.

low-resource

Offline-First Inference

Full capability on-device — no cloud required. Built for rural deployments with intermittent connectivity.

offline

Sub-4ms Edge Inference

Real-time speech-to-speech AI deployed at the network edge — zero latency, zero cloud dependency. The core engine behind every LocaleNLP product.

[ CORE ENGINE ]

Speech-to-Speech Systems

End-to-end voice translation across language pairs with sub-4ms edge latency.

speech

Community Data Engine

Lughatna — a crowdsourced linguistic data platform that pays native speakers to contribute voice recordings and translations.

lughatna

Multimodal AI

Vision-language models trained on African visual contexts — not proxies adapted from Western datasets. Document understanding, scene description, and OCR for scripts with no existing commercial support.

multimodal
[ Architecture ]

Nine systems. One infrastructure layer.

Every component is purpose-built for low-resource language environments — not adapted from English-dominant pipelines.

Tokenizer
Morpheme-aware BPE for agglutinative scripts
01
ASR Engine
CTC decoding for 50+ language variants
02
TTS Engine
Tonal contour synthesis, ONNX export
03
Translation
Community-corpus neural MT, 600+ pairs
04
AfriLION Core
Foundation model — 38 language families
Core
05
Language ID
200+ dialect classification, text + audio
06
Edge Runtime
INT4/INT8 inference, <4ms ARM latency
07
Data Pipeline
Collection → Validation → Tokenization
08
Community Platform
Lughatna — 3,200+ native contributors
09
Product Layer

From language to intelligence

example.ts
import LocaleNLP from '@localenlp/sdk';

const nlp = new LocaleNLP({ key: process.env.LOCALE_API_KEY });

// Swahili → English + Arabic
const result = await nlp.translate({
  text: "Habari za asubuhi",
  source: "sw",
  targets: ["en", "ar"],
  mode: "contextual"
});

// { en: "Good morning", ar: "صباح الخير", confidence: 0.96 }
Urgency Signal

This problem is accelerating

2.6B
people lack reliable internet
Yet AI products assume cloud connectivity as baseline. These users are not a niche — they are the majority.
95%+
of AI training data is English-derived
Models trained on this data fail catastrophically on morphologically rich African languages and Arabic dialects.
3,000+
languages facing extinction by 2100
Language death accelerates as communities adopt dominant-language tech. Digital presence preserves cultural vitality.
7B
mobile-first users by 2030
The next billion internet users are mobile-only, in low-resource language regions. The infrastructure must exist before they arrive.
Real-world Impact

When AI speaks every language, everything changes

Agricultureha · Hausa

A smallholder farmer asks about soil conditions — in Hausa.

Voice AI interprets the query, cross-references local agronomic data, and responds with rainfall forecasts and crop timing advice — all offline, in dialect.

Educationam · Amharic

A student learns mathematics in Amharic, not English.

Mother-tongue education dramatically improves retention. LocaleNLP provides the language layer that makes adaptive learning software culturally coherent.

Healthcaresw · Swahili

A nurse collects patient history in Swahili — accurately.

Medical translation errors kill. Native-language symptom collection with clinical terminology validation prevents misdiagnosis at the first point of contact.

Get Started

Build for the world that AI forgot.

Join research institutions, NGOs, and technology companies building on LocaleNLP's language infrastructure.

Join Lughatna — contribute your language →
2024
Founded
50+
Languages
12M+
API calls / month
8,400+
Community contributors