Natural Language Processing
Natural language processing
Systems that read, sort and extract information from text at volume — emails, forms, tickets, WhatsApp messages.
The problem
Your inbox, your helpdesk queue, your WhatsApp forwarding group — all of them fill up daily with text that a person has to read and decide what to do with. Some of it is a complaint, some is an order, some is a payment query, some is junk. Right now a person makes that call for each one, then decides who it goes to and what information to pull out of it. That reading-and-sorting work is invisible because it happens in someone's head, but it adds up to real hours every week.
Text that used to pile up gets sorted, labelled and filed automatically.
Built for: Back offices processing high volumes of unstructured text every day.
What we deliver
Classification and routing. Emails, tickets and messages are sorted into your categories automatically — complaint, order, enquiry, escalation — and sent to the right queue or person without a human reading each one first.
Field extraction. Names, amounts, order numbers, dates, GST numbers — whatever your downstream process needs is pulled out of free text and placed into a structured field.
Hindi and Hinglish support. Indian business communication mixes English, Hindi and transliterated Hinglish in the same message. We build on models that handle this mix, not on English-only tools that break on the first mixed sentence.
Volume without a headcount increase. The system processes as many messages as arrive, at the same quality, without a proportional cost increase. It does not get slower on Monday mornings or before the festival season.
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Frequently asked questions
- Is this the same as building a chatbot?
- No. A chatbot is designed to hold a conversation. NLP for back-office work is about reading text that already arrived — a batch of emails, a day's worth of tickets — and turning it into structured data or routing it correctly. No conversation, no user interface, just text in and organised output out.
- Can it handle regional languages beyond Hindi?
- It depends on the language and the task. Hindi and English and their common mix work well. Other Indian languages depend on how much labelled text is available in that language for the specific task you need. We check this during scoping.
- What happens when it classifies something incorrectly?
- Incorrect classifications are the main thing we monitor after go-live. We set up a sample review where your team flags wrong calls, and those corrections are fed back to improve the model over time. The error rate should drop across the first few months of live use.