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A grand old library with an ornate ceiling and floor-to-ceiling shelves.

Natural Language Processing

Search, message sorting, summaries and replies that cope with English, Urdu in its own script and Urdu typed in Latin letters, often all in the same sentence.

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The problem

Where English-only software falls short.

Search that needs perfect spelling

The same word is spelled three different ways in Roman Urdu, and a keyword search only finds the one you typed.

Messages sorted by hand

Complaints, orders and sales leads arrive mixed together in one inbox.

Voice notes nobody has time for

Customers and staff send long voice notes that someone has to listen to and type up.

English-only software

Software built for English alone quietly ignores the messages customers find easiest to write.

What we build

Language tools that meet customers halfway.

Rows of labelled wooden drawers in an old card catalogue.

Natural Language Processing

Meaning-based search

Help centres and product catalogues that find the right answer whatever spelling or script is used.

A man typing a message on his phone with a bag on his lap.

Natural Language Processing

Message routing

WhatsApp and email messages sorted into complaints, orders, prices and leads, then sent to the right person.

Three men in conversation on a city street.

Natural Language Processing

Voice-note transcripts and summaries

Urdu voice notes transcribed and summarised in English for the team, with names and amounts checked.

A man in a suit reading the business pages of a newspaper.

Natural Language Processing

Feedback and review analysis

Reviews and survey answers grouped by topic and tone, so recurring problems stand out.

How it gets built

Building for the way people really write.

  1. 01

    Gather real messages

    A sample of real customer messages, with personal details removed, so the system meets the spellings and slang it will actually see.

  2. 02

    Define the categories

    What each message type means for your team and who should receive it.

  3. 03

    Choose the model

    A hosted or self-hosted language model, chosen by accuracy on your messages, cost and privacy.

  4. 04

    Measure on your data

    Accuracy per category and per script, including mixed English-Urdu sentences.

  5. 05

    Connect the channels

    WhatsApp, email, website forms or your CRM, wherever the text arrives.

  6. 06

    Keep a person in the loop

    Unclear messages go to staff, and their corrections improve the next version.

What the language system delivers.

  • Search or routing working on your channels
  • Support for English, Urdu and Roman Urdu
  • Accuracy report on your own messages
  • Review screen for unclear cases
  • Dashboard of topics and trends
  • Documentation and handover

Tools we use

  • LLMs
  • Embeddings
  • Whisper
  • Vector search
  • Python
  • n8n
Close-up of dark keyboard keys in low light.A woman reading a book on a rock by the water.

Questions

What people ask about Natural Language Processing

Sorting and routing messages, meaning-based search and short summaries work well. Reading printed Urdu from images and transcribing noisy audio are harder and need checks.

Questions? We will put you on the right path

Tell us what you want from Natural Language Processing and we will tell you what it would take.

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