spaCy

What it is

spaCy is a Python library for real-world natural language processing. Where NLTK is academic and slow, spaCy is engineered for shipping — pre-trained pipelines, blazing tokenization, named entity recognition, dependency parsing, lemmatization, and trainable custom components.

How Vaaani uses it

• Extracting structured fields (people, dates, amounts) from PDFs and emails
• Building custom NER for niche domains (legal clauses, medical entities)
• Cleaning and tagging large corpora before fine-tuning a transformer
• Production rule-based + statistical hybrid pipelines

Why it makes the cut

spaCy is what I reach for when the customer cares about latency and robustness, not just accuracy on a benchmark. It runs on CPU, handles edge cases, and integrates cleanly with every web framework.

Sample code

import spacy

nlp = spacy.load("en_core_web_trf")
doc = nlp("Vaaani builds custom AI workers for businesses worldwide.")

for ent in doc.ents:
    print(ent.text, ent.label_)
# Vaaani ORG