Translate into the languages your users actually speak
Tarjumi is a neural translation API for African languages. 16 languages, one API call. Built by CIF AI.
Built to work. Grounded in research.
Each product comes from real problems we encountered — and solved. No vaporware. If it's listed here, it either works today or is being built in the open.
Neural machine translation for 16 African languages. Kikuyu, Swahili, Luo, Luhya, Kalenjin, Somali, Amharic, Yoruba, Zulu, and more — the languages other translation APIs forgot. One API call. Production-ready.
More from CIF AI
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Coming soonPre-launch security audits and hardening. Convergent testing — two independent paths to every vulnerability.
Coming soonInfrastructure for organisations that need to trust their denominators. No more "how many people do we actually serve?"
Coming soonResearch-first. Product-second.
We don't build tools and then look for problems. We study systems, find the structural gaps, and build only what closes them.
Every system tells you two stories — what it reports and what's actually happening. We build tools that listen to both.
The distance between what you expect and what you see isn't error. It's information. Our products are built to surface that gap and make it actionable.
Offline-first. Self-hostable. Designed for the places where reliable infrastructure doesn't exist yet — because that's where the work matters most.
The framework behind the products
Our products are grounded in original research — a formal framework for measuring convergence across independent observations. The mathematics, the proofs, and the empirical results are coming.
The Convergence Identity — paper forthcoming
A domain-agnostic mathematical framework that formalises how independent measurement paths validate (or invalidate) each other. The theory behind our products. Details soon.
Agent-based simulation for testing convergence under controlled conditions. How do independent measurement systems behave at scale? What breaks first?
Applying convergence methods to meteorological data. Multiple forecast models, multiple ground-truth sensors — the gap between them tells you where the forecast is weakest.
Two sensor networks watching the same fault line. When an earthquake hits, the gap between them reveals blind spots, tracks wave propagation, and separates instrument noise from real ground motion.