AI Agents & Automation

Natural Language Processing

Pipelines that classify, extract, summarize and search your text in the languages you work in, with measured accuracy and human review where the model is unsure.

Discuss your projectDiscuss your NLP project

Turn unstructured text into usable data

Emails, tickets, contracts, reviews and transcripts hold information your systems can't use until someone reads them. We build NLP pipelines that classify, extract, summarize and search that text, using libraries such as spaCy and Hugging Face transformers, large language models, or a mix. Pipelines work in English and other languages, including Arabic, Hebrew and Bengali, are measured on labeled examples from your own data, and send uncertain cases to people instead of guessing.

AI-assisted, expert-validated NLP

How AI assists

  • AI pre-labels text for annotation; your domain experts confirm or correct the labels.
  • LLMs draft extraction prompts and test cases, compared against trained models on your data.
  • Coding agents draft pipeline code, connectors and evaluation scripts for engineer review.
  • AI groups errors by type and language to show where the pipeline struggles.

What our experts own

  • Label definitions and the guidelines annotators follow
  • The model or prompt for each task, chosen on measured quality and cost
  • Confidence thresholds, and which items go to human review
  • Handling of personal data: redaction, access and retention

Where each approach usually fits

Illustrative starting points; the final choice per task comes from tests on your own labeled text.

RulesTrained modelLLM promptingHuman review
Language detectionFits some casesUsually a good fitRarely the right choiceRarely the right choice
Topic and intent classificationFits some casesUsually a good fitUsually a good fitFits some cases
Names, dates and amountsFits some casesUsually a good fitFits some casesFits some cases
Contract clause extractionRarely the right choiceFits some casesUsually a good fitUsually a good fit
Sentiment and themesRarely the right choiceUsually a good fitFits some casesFits some cases
Long-document summarizationRarely the right choiceFits some casesUsually a good fitFits some cases
  • Usually a good fit
  • Fits some cases
  • Rarely the right choice

Who has text to process

People on your team read every incoming email, ticket or contract just to decide where it goes or to pull out a few details, and the backlog grows faster than they can sort it.

  • Support operations sorting incoming tickets and emails by topic and urgency
  • Legal and procurement teams pulling dates and terms out of contracts
  • Insight teams reading reviews and surveys written in several languages

What you receive

NLP pipelines built around your text

  • Multilingual processing

    Language detection, translation steps and per-language evaluation, covering English, right-to-left Arabic and Hebrew, Bengali and other languages you need.

  • Entity and data extraction

    Names, organizations, dates, amounts and your custom entities pulled from contracts, emails and transcripts into structured fields.

  • Classification and routing

    Intent, topic and priority classification that routes tickets and messages to the right queue, with uncertain cases sent to a person.

  • Sentiment and theme analysis

    Aspect-based sentiment and topic clustering across reviews, surveys and tickets, showing what people mention and how they feel about it.

  • Summarization and search

    Summaries of long documents and transcripts with links to source passages, plus semantic search across your text collections.

  • APIs, batch jobs and streams

    Pipelines delivered as APIs, scheduled batch jobs or stream processors, connected to your databases, document stores and apps.

Where text processing stops

  • Conversations with customers belong to AI Chatbots, and question answering inside your product to LLM Integration; NLP here processes text in bulk.
  • Scanned pages, photos and handwriting need OCR first; that step is scoped under Computer Vision, and NLP takes over once the text is machine-readable.
  • Domain judgment stays with your experts: we organize labeling and measurement, but legal, medical or policy interpretation of your text comes from your side.
  • Translation steps serve analysis; documents translated for publication or legal use need professional translators, which this service doesn't provide.

Typical text-processing requests

Typical scenarios we scope, not client case studies.

  • A shared inbox sorted by hand

    Someone triages a shared inbox every morning. We would classify messages by topic and urgency, send confident ones straight to the right queue, and keep unclear ones in a review list with the suggested label shown.

  • Key terms buried in signed contracts

    Procurement needs renewal dates, notice periods and liability limits from signed contracts. We would extract each field with a link to the clause it came from, and send low-confidence fields to a reviewer before they reach your contract register.

  • Reviews arriving in several languages

    A product team gets reviews in English, Arabic and Bengali. We would detect each review's language, tag the aspects mentioned and the sentiment toward them, measure quality separately per language, and group recurring themes for the team to read.

How an NLP project runs

  1. 01

    Task and data review

    Define each task, its labels and the decisions it feeds, review sample text in every language involved, and agree the quality bar.

  2. 02

    Labeling and baselines

    Label a test set with your experts, then compare approaches, from rules and spaCy models to fine-tuned transformers and LLMs, on quality and cost.

  3. 03

    Pipeline integration

    Build the pipeline as an API, batch job or stream, with confidence thresholds, human review for uncertain items and links to your systems.

  4. 04

    Monitor and improve

    Track quality, drift and corrections in production, and retrain or adjust prompts when the language in your data shifts.

Two ways to work with AI tools

Choose where AI coding agents may process your code while we build. The engineering standard is the same either way.

Not sure? We'll recommend one during scoping. Compare AI delivery options

How design, QA and operations support your pipeline

  • Review screens for your staff

    Designers build the screens where staff check extracted fields, correct labels and accept summaries, so corrections are quick and feed back into evaluation.

  • QA with labeled test sets

    QA keeps labeled test sets per task and language, tracks precision and recall per class, and reruns regression checks after model, prompt or pipeline changes.

  • Monitored pipelines

    DevOps runs the pipelines with queue monitoring, retries, alerting and cost tracking, on hosted APIs or on private infrastructure when text must stay in your environment.

  • Quality maintained over time

    We watch for drift, such as new products, terms or languages in your text, review corrections with you, and update models or prompts after regression checks.

FAQ

Frequently asked questions

Which languages can you support?

English and many others, including Arabic, Hebrew and Bengali. Quality varies by language and task, so we evaluate each language you need on your own text before committing, and plan extra labeling where a language has fewer ready-made models.

Should we use an LLM or a custom NLP model?

It depends on volume, cost, speed and how stable the task is. LLMs handle varied tasks with little labeled data; smaller trained models are often cheaper and more consistent at high volume. We compare both on your test set, and many pipelines combine them.

How accurate will it be?

We can't promise a number before seeing your data. We agree target metrics with you before development, measure them on a test set your experts have checked, and route low-confidence results to people instead of passing them on as certain.

Can sensitive documents stay inside our environment?

Yes. Pipelines can run in your cloud account or on private infrastructure with open-weight models, and personal data can be redacted before any external call. For our development work, Private / Local AI Engineering runs the AI coding models inside an agreed boundary; Claude Code / OpenAI Codex Engineering uses commercial coding agents under agreed account terms.

View all case studies
View all projects
View all articles

Put your text data to work

Send a sample of the text you handle. We'll suggest which tasks suit NLP, which approach fits each one, and how quality will be measured.

Discuss your projectTalk to an NLP engineer