Natural Language Processing (NLP)
Natural language processing (NLP) is how software makes sense of human language — extracting entities, classifying topics, gauging sentiment, and turning messy text into structured, usable data. Large language models have made NLP far more capable and far easier to apply to real business text.
What is Natural Language Processing?
We build NLP into products and pipelines: analyzing support tickets, contracts, reviews, and documents to surface insight and automate work. Whether it's tagging thousands of records or understanding intent in real time, we turn unstructured language into something your systems can act on.
How Natural Language Processing works
Define the language task
Extraction, classification, sentiment, or intent — we scope the exact output.
Process the text
We clean and structure your text so it's ready to analyze.
Apply the right model
LLMs or specialised models perform the analysis at the accuracy you need.
Deliver structured data
Results flow into your systems as clean, structured, actionable data.
What we build with Natural Language Processing
Entity extraction
Pull names, dates, amounts, and terms from documents.
Sentiment analysis
Gauge how customers feel across reviews and tickets.
Text classification
Tag and route content, tickets, and emails automatically.
Intent detection
Understand what users want so systems can respond.
Document parsing
Turn contracts and forms into structured records.
Summarization
Condense long text into clear, useful summaries.
Natural Language Processing is a good fit for
Teams drowning in unstructured text
Support, legal, and finance document workflows
Products that classify or route content
Businesses wanting insight from reviews and feedback
Often built with
What does AI development cost?
Read our AI development cost guide for a full pricing breakdown, including ongoing inference costs.
Natural Language Processing — frequently asked questions
What is NLP used for?
NLP (natural language processing) is used to make software understand and process human language — extracting entities, classifying and routing text, analyzing sentiment, detecting intent, parsing documents, and summarizing. It turns unstructured text into structured, actionable data.
What's the difference between NLP and LLMs?
NLP is the broad field of processing language; LLMs are powerful models that have become the leading way to do NLP. Today many NLP tasks — extraction, classification, sentiment — are solved with LLMs, though lighter specialised models still suit high-volume, narrow tasks.
Can NLP process our documents automatically?
Yes. We build pipelines that read documents — contracts, forms, tickets — and extract the fields and insights you need as structured data, automating work that used to be manual and error-prone.
How accurate is NLP?
With modern models, accuracy on common tasks like classification and extraction is high, especially when tuned to your data. We evaluate against your real examples and add review steps where accuracy is critical.
Related AI capabilities
Ready to build with Natural Language Processing?
Tell us what you want to build and CodersArts Build will scope it into a fixed price and timeline — with evaluation and guardrails built in.