Text Data Audit & NLP Scoping
We review your documents, tickets and messages to pick NLP tasks that remove manual reading and deliver measurable savings.
- Corpus audit
- Use-case scope
AI, Machine Learning & Intelligent Automation
NLP Development Services
We turn emails, tickets, contracts and reviews into structured insight with classifiers, entity extraction, summarisation and sentiment models fitted to your domain language.
- Text classification
- Entity extraction
- Summarisation
- Sentiment analysis
- Years of delivery
- 8+
- Years of delivery
- Projects shipped
- 350+
- Projects shipped
- Clients across US, UK, AU & SG
- 200+
- Clients across US, UK, AU & SG
What’s included
Everything You Need to Get Value From Your Text Data
We build NLP pipelines that read, classify and summarise your documents, emails and conversations, then plug them into your workflows.
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Text Classification & Intent Detection
We route emails, tickets and requests automatically by topic, urgency or intent, so your teams answer the right items first.
- Classifier model
- Label taxonomy
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Named Entity Recognition & Data Extraction
We pull names, dates, amounts and clauses from contracts, invoices and reports into structured fields without retyping.
- Entity extractor
- JSON output schema
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Document Summarisation Pipelines
Long reports, calls and case files become short, accurate summaries that let your staff decide faster.
- Summariser service
- Quality benchmarks
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Sentiment & Customer Feedback Analysis
We analyse reviews, surveys and support chats to surface what customers like and dislike, tracked over time.
- Sentiment model
- Insights dashboard
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Multilingual Search & Translation Models
We build search and translation across the languages your customers use, so content works in every market you serve.
- Multilingual search
- Language packs
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NLP API Integration With CRM & Helpdesk
We connect NLP services to Salesforce, Zendesk or your own apps so results appear where your team already works.
- REST API
- Helpdesk connector
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Accuracy Testing & Model Maintenance
We measure precision and recall on your data, then retrain as language, products and terminology change.
- Evaluation report
- Retraining plan
Need something specific?
Why Kramate
Why Business Owners Choose Kramate for Text and Language AI
We turn your piles of emails and documents into answers your team can use.
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We Start With Your Own Documents
Instead of
tools trained on strangers' writing
With Kramate
We learn from your emails, forms and tickets so it understands your own terms.
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Works in the Languages You Sell In
Instead of: tools that only get English right
With Kramate: We test it with real messages in each language your customers write in.
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Mistakes Go to a Person
Instead of: wrong answers slipping through unseen
With Kramate: When it is unsure, it passes the item to your staff instead of guessing.
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Results Appear Where Your Team Works
Instead of: yet another screen to check
With Kramate: We connect it to the tools your staff already open every day.
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Checked Again as Your Words Change
Instead of: accuracy that fades over time
With Kramate: We recheck it on fresh examples now and then and fix it when needed.
Our approach
How We Build Your NLP Solution, Step by Step
We teach software to read your documents and messages, then check its reading against your own staff.
7 clear steps 7 deliverables you keep
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Text Sample Review
We read through your emails, tickets or contracts to see what language you use and which jobs take staff the longest.
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Task & Label Definition
We agree what the software should find or sort, such as topics, names, dates or customer mood.
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Example Labelling
Your experts and ours mark up a set of real examples, which the software learns from.
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Language Model Trial
We train and compare a few approaches on your labelled text, and show which reads your content best.
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Accuracy Check
We score its answers against your team on fresh documents, and list the kinds of text it still misreads.
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Connect to Your Tools
We link the results to your helpdesk, CRM or document store, so staff see them where they already work.
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Retraining Rounds
We review mistakes after launch and retrain as your products, wording and customers change.
Tools & technologies
Technologies We Use for NLP Development
We combine proven language libraries with modern models, chosen per language and task.
- spaCy: Production-grade library for text processing and entities.
- Hugging Face: Hub of open models and libraries for language and vision work.
- scikit-learn: Classic machine learning for prediction and classification.
- PyTorch: Framework for building and training neural networks.
- OpenAI: Provides GPT language models for text, code and reasoning features.
- Claude: Anthropic language models for careful reasoning and long documents.
- Google Gemini: Google models that handle text, images and long context.
- Python: Readable language for business logic, data and automation.
- pandas: Cleans and reshapes business data ready for modelling.
- Jupyter: Notebooks for exploring data and sharing findings.
- Label Studio: Annotation tool for labelling text, images and audio.
- Elasticsearch: Search engine for text, filters and hybrid retrieval.
- FastAPI: Python framework for fast, well-documented APIs.
- Docker: Packages software so it runs the same everywhere.
- AWS: Amazon cloud hosting and services.
- MLflow: Tracks experiments and manages model versions.
Expert guide
NLP Development Services: A Complete Guide for Businesses
NLP development services build software that reads, sorts, searches and understands human language in emails, documents, chats, reviews and recorded calls. Natural language processing turns unstructured text into fields, labels, answers and alerts that other systems can use.
Large language models have widened what is possible, yet many business text tasks are still better served by smaller, cheaper and more predictable models, or by a mix. The right choice depends on the task, the language, the volume and the cost of errors.
This guide maps common text tasks to techniques, explains how quality is measured, covers languages and privacy, and sets out what to ask when evaluating NLP development services for work in the USA, UK, Australia and Singapore.
Matching the text task to the technique
The same business goal can be met several ways. This table shows common tasks and the usual approach, which a partner should justify for your case.
| Task | What it does | Typical approach |
|---|---|---|
| Classification | Routes tickets, tags topics, flags sentiment | Fine-tuned compact model, or an LLM with examples |
| Entity extraction | Pulls names, dates, amounts and clauses from text | Trained extraction model, rules, or a model with a fixed output format |
| Semantic search | Finds passages by meaning, not exact words | Embeddings and a vector or hybrid index |
| Summarization | Condenses long text | Large language model with review |
| Speech to text | Transcribes calls and meetings | Speech recognition model plus cleanup |
| Translation | Converts between languages | Machine translation with human checks for key content |
What NLP development services typically deliver
- Document processing: reading invoices, contracts, claims and forms into structured data.
- Inbox and ticket triage: sorting and prioritizing messages and drafting responses.
- Enterprise search: finding policies, cases and answers across wikis and files.
- Voice-of-customer analysis: grouping reviews, survey comments and call themes.
- Conversational interfaces: see our AI chatbot development for assistants.
For generative drafting and question answering over your content, generative AI development is the related service.
NLP or large language model: choosing the right tool
A large language model handles many tasks with a prompt and few examples, which speeds up prototypes. A smaller model trained for one task can be cheaper per document, faster, simpler to host privately and more consistent. Rules still win for rigid formats such as tax numbers.
Many production systems combine them: rules for fixed patterns, a compact classifier for routing, and a large model only for hard cases. Model hubs such as Hugging Face (opens in a new tab) document thousands of open language models, and provider references such as the Azure AI documentation (opens in a new tab) cover hosted language services.
Languages, dialects and training data
Quality varies widely by language. For a stream of incoming documents, pair NLP with workflow automation so results reach the right system. English is best supported, while many other languages, dialects and mixed-language writing have weaker coverage. Singapore and Australia are good reminders: local expressions, code-switching and industry slang can confuse models trained on general text.
- Collect real samples of your own text, including messy and abbreviated writing.
- Label a representative set with written guidelines and check that reviewers agree with each other.
- Test each language separately instead of assuming results carry over.
- Plan for new vocabulary, such as product names, that appears over time.
Measuring quality and keeping a person in the loop
Two measures matter for most NLP systems. Precision asks how many of the items the system flagged were correct. Recall asks how many of the items it should have flagged it found. Raising one often lowers the other, so choose the balance using the cost of each type of error.
Build a labeled test set from your own text and keep it separate from training. Report results by category, language and channel. Generated outputs such as summaries need sampled human review, and low-confidence results should go to a person. The NIST AI Risk Management Framework (opens in a new tab) and the OECD AI Principles (opens in a new tab) both emphasize accountability and human oversight. Nobody can promise a quality figure before testing on your documents.
Privacy, security and regional rules
Text often contains names, health details, account numbers and opinions. Redact or mask sensitive fields before analysis where possible, limit who can see raw text, and check where each model service processes it. The ICO’s AI and data protection guidance (opens in a new tab) is a helpful reference on lawful basis and minimization.
- USA: HIPAA for health text, state privacy laws such as California’s, and rules on call recording consent vary by state.
- UK: UK GDPR and the Data Protection Act 2018.
- Australia: the Privacy Act 1988, plus state rules on recording conversations.
- Singapore: the Personal Data Protection Act, including consent and purpose limits.
Confirm details with legal counsel, especially for recorded calls and cross-border processing.
Industry use cases
- Insurance: extracting claim details from emailed forms and attachments to prefill a case system.
- Legal: finding similar clauses across a contract library for a lawyer to examine.
- Healthcare administration: routing patient messages by topic and urgency to the correct staff queue.
- Hospitality: grouping guest reviews by theme so managers see recurring issues.
These are illustrative examples, not Kramate client results.
Cost factors and budgeting
Cost follows the number of tasks and languages, the labeling effort, document volume and variety, accuracy needs, integration with existing systems and hosting. Per-document processing fees for hosted models grow with volume, so estimate monthly usage early.
Start with a pilot on a sample of real documents, compare approaches on the same test set, then scale the one that balances quality and cost. Fixed price fits the pilot, and a dedicated team suits a growing portfolio of text tasks.
Choosing among NLP development services providers
- They compare more than one technique on your own documents.
- They show precision and recall by category, not a single headline score.
- They support your languages and can explain the gaps.
- They describe how sensitive text is masked and where it is processed.
- They hand over models, labeled data and evaluation scripts.
How Kramate delivers NLP projects
Begin with a free consultation; a detailed estimate follows within 24 hours, and an NDA is agreed before you share sample documents. A project manager is assigned to your work, you receive daily Slack or Teams updates, and each two-week round closes with a demo. Fixed-price, dedicated-team and hourly options exist, and intellectual property passes to you. Kramate can lead delivery or embed with your team, as set out in our approach.
Frequently asked questions
What is natural language processing used for in business?
Common uses include sorting emails and tickets, extracting data from documents, searching knowledge bases by meaning, analyzing reviews, transcribing calls and drafting summaries. Each reduces manual reading and typing for staff, and each can start small with one document type before expanding to others.
What is the difference between NLP and an LLM?
NLP is the whole field of computers working with language. A large language model is one kind of NLP model, flexible but costlier. Many tasks suit smaller specialized models or rules, and production systems often mix all three.
How do you extract data from documents with NLP?
Convert files to text, using OCR for scans, then apply a trained extraction model, rules or a language model with a fixed output format. Validate fields against business rules and route doubtful results to a person.
How do you measure NLP model quality?
Use a labeled test set from your own text and report precision and recall per category, plus language and channel. For generated text, add sampled human review against written criteria.
Can NLP handle multiple languages?
Often, but support is uneven. Test each language and dialect separately, include local vocabulary in samples, and consider human review for languages with weaker model coverage or specialized industry vocabulary.
How do you protect personal data in text analytics?
Mask or redact sensitive fields before processing, limit access to raw text, choose processing locations deliberately, set retention limits and document your lawful basis for processing. Confirm requirements with legal counsel in each country.
Sources and further reading
- Hugging Face Documentation (opens in a new tab)Hugging Face
- Azure AI documentation (opens in a new tab)Microsoft
- AI Risk Management Framework (opens in a new tab)NIST
- OECD AI Principles (opens in a new tab)OECD
- Guidance on AI and data protection (opens in a new tab)ICO UK
- AWS Responsible AI (opens in a new tab)Amazon Web Services
FAQ
Frequently asked questions
Price depends on the NLP task (classification, entity extraction, summarisation, sentiment), languages, text volume, how much labelled data exists, accuracy needs, use of large language models versus smaller models, and integration. Domain-specific text such as legal or medical content needs more tuning.
They normally go through text sampling and requirement setting, labelling, model building, testing against real documents and deployment. Timing is affected by text quality, number of languages, labelling effort and how the results must feed other systems.
Yes. Kramate signs an NDA before detailed discussions and transfers trained models, annotation guidelines, labelled data, processing code and documentation to you. Your text data stays confidential and is not used to train models for other customers.
Yes. We expose NLP as an API or batch process that reads from your inbox, ticket system, document store or database and writes results back. Your teams keep their tools while tagging, routing or summarising happens in the background.
Large language models handle varied language with little setup but cost more per request. Smaller models can be faster, cheaper and easier to run privately for fixed tasks like tagging. We test both on your text and recommend based on accuracy, speed and running cost.
Yes, with care. We check language coverage on your own samples and adapt models with domain terms, abbreviations and examples. Accuracy can vary by language, so we measure each one separately instead of assuming results carry over.
We label a sample of your real text, keep it separate from training, and score the model on it using measures suited to the task, such as precision and recall for entity extraction. Reviewing errors with your team shows which mistakes matter most.
Taking new projects
Let’s talk about your NLP Development Services needs
Tell us about your project and get a free consultation and estimate within 24 hours.
- We reply within 4 business hours A real consultant reads your brief — no bot, no queue.
- 30-minute discovery call We dig into goals, constraints and budget. NDA signed on request.
- Proposal in 24 hours Scope, timeline, team and price — clear enough to decide on.
NDA on request You own the IP No obligation Free estimate