Natural Language Processing (NLP)

What is natural language processing (NLP)?

Natural language processing covers the methods computers use to work with text and speech written for people rather than for machines. Most business information lives in this form: emails, support tickets, contracts, reviews, call notes and reports. NLP turns it into something software can sort, search and act on. Without it, that information can only be handled by people reading it one item at a time.

Classic NLP broke the problem into steps: split text into words, tag grammar, recognize names and dates, then apply rules or a trained machine learning model for one specific task. Modern NLP is dominated by large language models, which handle many of these tasks from a single model guided by instructions and examples.

In a business, NLP shows up in ticket routing, spam and intent detection, invoice and contract extraction, search that understands meaning rather than keywords, summaries of meetings and documents, chat assistants and analysis of customer feedback.

What are common NLP tasks?

Most business uses of NLP combine a few core tasks:

• Classification: assign a label, such as topic, urgency or intent, to a message or document.
• Entity extraction: pull out names, dates, amounts, product codes or addresses into structured fields.
• Sentiment analysis: judge whether text is positive, negative or mixed, and about what.
• Summarization and search: condense long threads or calls, and find relevant passages by meaning rather than exact words.
• Translation and generation: render text in another language or draft new text from instructions.

Choosing the right task framing matters more than choosing a model. A vague goal like understanding customer emails becomes workable once it is broken into specific labels and fields.

How is NLP different from large language models?

NLP is the field; large language models are one family of tools within it, and currently the most capable general-purpose one. Before them, teams typically built a separate model for each task, trained on labeled examples. An LLM can do many tasks from instructions alone, which makes it much faster to get started.

Smaller, task-specific NLP models still have a place. They can be cheaper and faster at high volume, easier to run inside your own infrastructure, and more predictable for narrow jobs like detecting language or tagging known entity types. A common pattern combines both: a lightweight model or rule handles the routine bulk, and an LLM handles ambiguous or open-ended cases. The right choice depends on volume, accuracy needs, speed, cost and where the data is allowed to go.

What are the limits of NLP?

Language is ambiguous, and NLP systems inherit that. Sarcasm, industry jargon, abbreviations, mixed languages and poor-quality scans all lower accuracy. Models trained mostly on English may perform worse in other languages or dialects, which matters for businesses serving several markets.

LLM-based NLP adds its own risks: it can produce fluent but wrong output, known as AI hallucination, and results can vary between runs. Bias in training data can show up in both classification and generation. Privacy is a further concern, because the text being processed often contains personal or confidential information.

These limits are manageable with clear task definitions, test sets built from your own data, validation of structured outputs and human review for uncertain cases.

How do you get started with NLP in a business?

Pick one text-heavy process where people spend time reading and sorting. Good candidates are shared inboxes, support queues, incoming documents and open-ended survey responses. Define the output precisely: which labels, which fields, what counts as correct.

Gather a sample of real examples and label part of it by hand, which gives you both a test set and a sense of how often people themselves disagree. Try an LLM with clear instructions first, since it is quick to set up, and measure it against your labels. If accuracy, cost or speed falls short, consider a smaller specialized model or a hybrid. Finally, connect the output to where work happens, such as routing a ticket in the helpdesk or writing fields into the CRM, because NLP only pays off when its results change what happens next.

How does Vizio AI use NLP?

NLP sits behind much of the work our DX Studio does on AI-powered internal workflows: classifying requests and tickets, processing documents, summarizing research, and supporting reporting and content operations. We build these around the tools a business already runs, connecting the output to CRM, finance, marketing or support systems through API integrations so that labels and extracted fields land where people actually work.

We also support these systems after launch, adjusting them as the language, volume and mix of incoming text change. A good starting point is mapping one text-heavy process and collecting real examples from it, which shows quickly whether the task is a good fit.