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Domain-aware translation

Translation that speaks your sector's language

General-purpose translation is great for a menu and mediocre for a cholera-response field guide. We pair a configurable AI model with a translation memory of real, in-domain examples.

Free for non-commercial humanitarian & social-impact use · no account needed to start

BetaCurrently in beta — translations are actively tested and human-verified as we refine quality.

How it works

Grounded in real, in-domain examples

For every piece of text, Translate retrieves the closest human-verified examples from the domain's translation memory and hands them to the model as guidance — so it phrases things the way professionals in the field actually do.

Your text

A sentence, a paragraph, or a whole document

Retrieve from memory

Full-text + dense vector search find the closest in-domain examples

Assemble domain prompt

Verified examples + subject-tuned instructions

Translation model

Self-hosted Gemma · private · EU-resident

Grounded translationResult

Right terminology, right register

grounds every translation
Curated by experts

WASH specialists & professional linguists — built independently of your text

Human-verified TMX corpus

The domain's curated translation memory

The translation memory

Getting the words right

Where generic translation slips, domain memory holds

General-purpose engines translate WASH terminology literally. Anchored in a sector corpus, Translate reaches for the accepted term instead.

English source

water safety plan (WSP)

Generic MT

Domain-grounded

Illustrative EN→FR examples. Pulled from the WASH translation memory in production.

The proof

Measurably better on domain content

We benchmarked the engine against the tools teams reach for today — DeepL, Google, Lara — and frontier LLMs, on held-out WASH test sets across 12 languages.

Automated neural metrics — for now. Scored with XCOMET-XL, a model-based quality metric, not human ratings. We're now working with native-speaking domain/sector professionals who score these outputs against human validators to ground the numbers in expert judgment. Full method on the benchmark page.

85.1
Mean XCOMET-XL
#1 of 8 engines
12/12
Beats the base model
in all 12 languages tested
12/12
Top-3 ranking
in 12 of 12 languages

Mean score vs. the engines teams use today

XCOMET-XL
Our engine
85.1
Gemini 3 Flash
84.6
DeepL
83.8
Google Translate
83.5

Zoomed axis — bars start at 82, not 0, so the gap between engines is visible.

Built to grow

One engine, many domains

WASH is the first live domain — but the domain is a first-class dimension, not a fork. New domains slot in with their own corpus, prompt, and best model per language.

One codebase
One translation engine

Domain is a dimension, not a fork

WASHLive

Water, Sanitation & Hygiene — its own corpus, prompt & best model per language

NutritionPlanned

Same pattern, new corpus

HealthPlanned

Same pattern, new corpus

LogisticsPlanned

Same pattern, new corpus

General

No translation memory — for off-domain text

Future-proof

Gets better as the models do

We continuously test new base models as they're released — and the moment a better self-hosted, secure model proves out on our evaluation set, we adopt it. Your translations keep improving, while your text never leaves our private, EU-resident infrastructure.

Continuously evaluated

New base models — like Gemma — are benchmarked on our domain evaluation set the moment they're released. We measure, not assume.

The best model wins

When a stronger self-hosted model proves out, we adopt it. Your translations keep getting better over time — no migration, no action needed on your end.

Privacy is never traded

Every upgrade stays self-hosted, secure, and EU-resident. Quality goes up; your data exposure stays at zero.

Why teams trust it

Private, resilient, and free for non-commercial impact

Private & EU-resident

Self-hosted models keep your text out of any big-tech AI cloud — and database and document storage stay in the EU region

Frugal & lower-impact

We choose small base models like Gemma — 26B parameters, not the trillion-plus behind ChatGPT or Gemini — so each translation uses a fraction of the energy, running in low-carbon data centres

Free for impact

Free for non-commercial humanitarian, non-profit & public-interest work

Translate your first paragraph now

No account needed to start. Text, documents, or a metered REST API — all on one shared engine.