Urdu NLPSolutions
Language AI that handles Urdu script, Roman Urdu and the code-mixed English that real customers actually write.
Urdu NLP Solutions: what the work involves
Most off-the-shelf language tools are tuned for English. Real messages from Pakistani customers mix Urdu script, Roman Urdu spelled five different ways, English nouns and the occasional emoji. A complaint written as a casual Roman Urdu sentence may be filed as spam or neutral, and keyword filters miss it because nobody spells the same word twice.
We begin by collecting a sample of your actual text and labelling it with you: topics, intents, sentiment, entities. That becomes the test set. We then compare options on it: a large multilingual model with careful prompts, a smaller fine-tuned classifier such as an XLM-R or MuRIL style encoder, or a hybrid. Normalisation steps handle spelling variants, diacritics and transliteration. Outputs include a confidence value, and low-confidence items go to a human queue whose corrections can retrain the model. Deployment is a small API, and we report accuracy per category and per script in plain terms, including where it is weak.
Core features
Script and spelling normalisation
Urdu script, Roman Urdu and mixed text are cleaned and unified, so spelling variants map to the same meaning.
Intent and topic classification
Messages are sorted into categories you define, such as complaint, order query or request, in either script.
Entity extraction
Names, cities, amounts, product names and dates are pulled from free text into structured fields.
Urdu summarisation
Long threads, articles or call notes are condensed into short summaries with the key points kept.
Semantic search in Urdu
Multilingual embeddings let people search by meaning, so an English question can find an Urdu document.
Evaluation on your data
Quality is reported on a labelled sample from your own messages, split by script and category.
What we get right before launch
Uneven quality across scripts
Models usually perform better on Urdu script than on casual Roman Urdu, and worse on dialect and slang. We measure each separately and set thresholds accordingly.
Limited labelled data
Public Urdu datasets are small and rarely match your domain. We build a small labelled sample with your team and use it for both evaluation and light adaptation.
Sensitive or political content
Classification of abusive, religious or political text carries real risk if mistaken. Such categories route to human review, and we avoid automated penalties on that basis alone.
Tools and technology
- Python
- Hugging Face Transformers
- OpenAI
- Google Gemini
- scikit-learn
- FastAPI
- pgvector
- PostgreSQL
Common questions, answered
Does it understand Roman Urdu?
Large models handle it reasonably, but spelling variation causes errors. We test on your real messages, add normalisation and examples for tricky phrases, and show you where accuracy falls short before launch.
Do we need to train a custom model?
Often not. A multilingual model with good prompts and a small labelled test set is enough for many tasks. Fine-tuning a smaller model makes sense for high volumes or strict privacy needs.
Can it run on our own servers?
Smaller open models can be self-hosted with suitable hardware, keeping text in-house. They may trail the largest hosted models on subtle tasks, so we compare on your data first.
How much sample data do you need?
Typically a few hundred to a few thousand representative messages to build a useful test set, depending on how many categories you have. More helps, but we begin with what you have.
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Ready to start your Urdu NLP Solutions project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.