AI glossary for businesses
Natural language processing (NLP)
In short
Natural language processing (NLP) is the field of AI that lets computers read, interpret and produce human language, written or spoken. It powers spam filters, translation, sentiment analysis, chatbots and today's large language models. For a business, NLP turns emails, reviews and documents into something software can sort and act on.
How NLP works
Computers handle numbers well and words badly. NLP bridges that gap. It breaks text into small pieces, turns them into numbers and learns patterns from huge amounts of examples. From those patterns it can tell what a sentence is about, whether the tone is positive or negative, and which names, dates or products it mentions.
Think of an experienced shop assistant who has read thousands of customer notes. They no longer read every word. They spot "late delivery" or "wrong size" at a glance and know where the note should go. NLP does the same, at a much larger scale and without getting tired.
Older NLP relied on hand-written rules and narrow models trained for one task. Modern NLP is mostly driven by large language models, which can summarise, classify, translate and draft text from one plain instruction.
NLP in practice for a small business
Picture a Greek e-shop selling cosmetics that receives hundreds of reviews and messages a month, in Greek and English. With NLP, it can tag each review by topic (packaging, delivery, scent, price) and by sentiment, then send a weekly summary to the owner. Recurring complaints show up in days instead of months. The same approach works for sorting support emails or pulling key fields from contracts. See the use case on AI for customer review analysis for a step-by-step version.
Tools such as ChatGPT, Claude or Google Gemini can do this with a well-written prompt. For higher volumes, an automation tool like Zapier or Make can pass each message to the model and write the result to a spreadsheet or CRM.
Why it matters and what to watch out for
Greek is harder. Most models are trained mainly on English text. Greek results are usually good, but test slang, Greeklish and regional terms before you trust them.
Errors sound confident. A model can mislabel sarcasm or add a detail to a summary that was never there. Spot-check a sample every week.
Personal data. Reviews and emails often contain names, phone numbers and order details. Under GDPR you need a legal basis and a tool with suitable data terms. Remove personal details where you can, and check with your DPO or legal advisor if you process customer messages at scale.
Cost. Usage is cheap per message but adds up with volume. Measure it in a small pilot first.