使用自动本地化

Example:

‍You’re translating UI strings from English to Japanese and English to Spanish.

for English → Japanese
the system picks Model A for its precision with short technical strings.

for English → Spanish
it uses Model B for fluent, marketing-friendly output.

Result:

Each string is routed through the best-performing engine for its specific language pair and content type, without manual configuration.

Example:

Your team uploads a batch of marketing copy in multiple languages.
Algebras runs a background quality check:It compares Model A (e.g. Google Translate), Model B (e.g. DeepL), and Model C (e.g. NLLB by Meta) on the same text segments.Using reference translations, linguistic scoring, and in-context evaluation, it selects the best output.

for English → Korean
DeepL may be chosen for smoother sentence flow in product descriptions.

for English → Arabic
NLLB might win due to better handling of dialectal nuances and right-to-left formatting.

Result:

Algebras detects that Model C outperforms the rest for your French marketing content and automatically uses it for all future similar content.

Example:

‍Your product contains recurring terms like: “Premium Plan”, “User Dashboard”, “AI Assistant”.
‍
Algebras automatically extracts these from your content and creates a glossary. Then, even across different contexts:

Try our Premium Plan today → consistently translates "Premium Plan"

the same way: 
Go to your User Dashboard → glossary-enforced translation

Result:

You can edit, approve, or lock glossary terms as needed — but the system handles the heavy lifting automatically.

Example:

You have a mobile app with a CTA button:
Get Started (80px width in design)

Default German translation:
Loslegen
→ fits perfectly

But in French:
Commencer maintenant
→ is too wide

Result:

Algebras detects the overflow and automatically adjusts:
→ Suggests Démarrer (a shorter, natural equivalent)
→ Ensures it stays under 80px and preserves UI alignment