Turkish natural language processing: building AI products in Turkish

Most AI models are trained mainly on English data. For startups building Turkish products, understanding the language's structure and models' Turkish performance is key to quality and cost.

Features of Turkish

• Agglutinative: suffixes let one word carry what is a whole sentence in English ("gidemeyeceklermiş").
• Rich morphology: many words derive from one root; root and suffix analysis matters for search and matching.
• Relatively free word order: meaning comes more from suffixes than order.
• Special characters: ç, ğ, ı, İ, ö, ş, ü; case conversion (I/ı, İ/i) often goes wrong.

Model performance

Current large models can do well in Turkish, but performance varies by model and task. Gaps in knowledge and language errors can appear in law, health or local regulation. Test with your own Turkish samples before choosing; see open vs closed models.

Token cost

Because many tokenisers are optimised for English, Turkish text can use more tokens for the same meaning, affecting cost and context window use; see AI costs.

Turkish evaluation

• Build your eval set from real Turkish user examples; see model evaluation.
• Score grammar, spelling, terminology consistency and formality separately.
• Test inputs with everyday language, abbreviations and typos.

Search and RAG tips

• Combine semantic and keyword search to catch root and suffix variation; see RAG.
• Normalise Turkish characters carefully (e.g. "ı" vs "i").

Opportunities

• Specialised solutions for Turkish public, legal, health and finance documents
• Assistants integrated with local workflows for Turkish SMEs; see AI for SME productivity
• Turkish speech and voice technology; see speech technologies
• Turkish education and content products

Conclusion

Turkish is both a challenge for AI products and an advantage for local startups. Teams that know the language and users can build quality global rivals cannot easily match.