Artificial Intelligence

Swahili NLP & LLMs

Models that actually understand Swahili.

We build the NLP stack that African products deserve: Swahili-first tokenizers, embeddings that capture morphology, and fine-tuned open-weight LLMs that perform on real Swahili tasks. Every model ships with an evaluation harness tied to your use case.

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38%

Fewer tokens vs. multilingual baselines

14.2 F1

Uplift on Swahili NER

12B+

Swahili tokens in corpus

7B–70B

Parameter range supported

What we ship

Capabilities

Tokenizers, embeddings and fine-tuned LLMs purpose-built for Swahili and East African code-switching — not bolted on to English-first systems.

01
Custom Swahili tokenizers & embeddings
Morphology-aware BPE trained on 12B+ tokens. Noun-class prefixes and agglutinative verb forms stay intact — cutting token count by up to 38% versus multilingual defaults.
02
Fine-tuned 7B–70B open-weight LLMs
We select, fine-tune and evaluate the right base model for your task — from lightweight on-device inference to full 70B reasoning stacks.
03
Swahili-first RAG over enterprise corpora
Retrieval pipelines wired to your data: pgvector indexes, hybrid BM25 + dense retrieval, and re-ranking tuned to Swahili document structure.
04
Eval suites built on real Swahili tasks
We ship model-agnostic evaluation frameworks — NER, QA, summarization, classification — so you can measure what matters before and after every update.

Outcomes

Drop-in Swahili understanding for your product
Chat, search and summarization that don't break on code-switching
Private, on-prem LLM options for sensitive data

Tech we use

PythonPyTorchHugging FacevLLMpgvector

In the field

1
Telco customer support
40k+ Swahili conversations per day handled without English fallback — deflection rate up 62%.
2
Legal document search
RAG over 20 years of Tanzanian court records — queried in Swahili, returned in context.
3
Code-switched moderation
Classifying Sheng/Swahili social posts where English-first models score near random.

Discuss your use case

How we deliver

Our delivery process

Every engagement follows the same rigorous four-stage approach — so you know exactly what to expect, and when.

Step01
Corpus audit
We map your data sources and gaps against our 12B-token Swahili corpus to identify what fine-tuning data you already own.
Step02
Tokenizer & embedding design
Morphology-aware BPE tokenizer trained on your domain, with embedding dimensions tuned for your retrieval use case.
Step03
Fine-tuning & evaluation
We fine-tune the right base model and run your eval suite — every model ships with benchmark numbers, not just vibes.
Step04
Deployment & monitoring
vLLM or TGI serving, on-prem or managed, with latency and drift dashboards wired in from day one.