Greek is compact, expressive, and occasionally messy for machines. Users switch between Greek and English mid‑sentence, drop accents, or type in Greeklish (“kalimera,” “eisai”). If you want assistants and search to feel native, you need more than a translation switch. Here’s a pragmatic guide to localizing AI business tools in Greece so they answer well—no matter how users type.
1) Normalize before you analyze
Start with robust normalization: lowercase, strip diacritics selectively, and canonicalize punctuation. Preserve original text for display, but run normalization for matching and indexing. Build a reversible mapping so you can highlight snippets exactly as the user wrote them.
2) Greeklish and transliteration
Handle Greeklish via a lightweight transliteration model: map characters (th → θ, x → χ) and learn common bigrams. Keep a confidence score; only auto‑convert when it’s high. Otherwise, search both forms. This dramatically improves intent detection and document retrieval in consumer support and e‑commerce.
3) Tokenization matters
General models sometimes fragment Greek words poorly. If you’re building custom components, train a tokenizer on modern Greek corpora. For retrieval, lemmatize common verbs and nouns so “αγοράζω,” “αγόρασα,” and “αγορά” relate. Better tokenization reduces embedding noise and speeds up inference.
4) Retrieval that respects context
Use a hybrid approach: BM25 for exact matches and dense vectors for semantic similarity. Index EL and EN content in the same collection with a language tag. At query time, detect language, expand with synonyms (including Greeklish variants), and score both sparse and dense results. Show which document and language the answer came from for transparency.
5) Conversational UX patterns for EL/EN
Default to the user’s first message language; switch only when they do. If the user pastes an English part number with a Greek question, the assistant should answer in Greek and quote the English item correctly. Provide quick chips for “See in English/Δες στα Ελληνικά” to give control without guessing wrong.
6) Prompts with boundaries and style
Create Greek system prompts that set tone (polite, concise), forbid speculation, and include fallback phrases for unknowns. Maintain a bilingual glossary for product names and legal terms so translations stay consistent. Use function calls for facts (prices, availability) rather than trusting the model’s memory.
7) Evaluation in Greek, not just English
Build a test set of EL/EN/Greeklish queries from real traffic. Score answers for accuracy, tone, and citation quality. Track “accent drop” errors—cases where missing diacritics cause mismatches. Review monthly with product and support to add new synonyms and clarify prompts.
8) Performance and cost
Hybrid retrieval plus a compact, multilingual model often beats giant monolingual setups on latency and price. Cache common answers, pre‑compute embeddings for FAQs, and batch vector queries. Expect €100–€400/month to run a lean stack at SME traffic levels.
9) Privacy and consent the Greek way
Use EU hosting, sign DPAs, limit retention, and let users opt out of training. Redact personal data automatically in logs. For sensitive categories, turn training off and review uploads. Label AI answers clearly and provide an easy “Talk to a person” path.
10) What great looks like
Users type fast, skip accents, or drop into English—and still get the right answer with a clear citation. The assistant stays in Greek unless asked otherwise, handles Greeklish without drama, and never hallucinates prices or policy. That’s localization that earns trust—and turns AI business tools in Greece into daily habits.