Thousands of unstructured text inputs -- support tickets, reviews, documents -- nobody is processing systematically?
Off-the-shelf NLP tools that don't understand your domain-specific terminology?
NLP Development Services
Natural language processing turns unstructured text -- emails, support tickets, contracts, clinical notes, user reviews -- into structured data your systems can act on.
We build NLP systems that classify, extract, summarise, and interpret text at scale. Not generic sentiment scores. Models trained on your domain vocabulary that understand what your customers, documents, and users are actually saying.
Document classification, entity extraction, sentiment analysis, and text summarisation
Fine-tuned models on your domain vocabulary and document types
Integration with your existing data pipeline, CRM, or operational systems
LLM-based and traditional ML approaches depending on volume and accuracy requirements
RaftLabs builds custom natural language processing systems for classification, entity extraction, sentiment analysis, and text summarisation on domain-specific data. We work with both traditional ML approaches (fine-tuned BERT, RoBERTa) for high-volume, latency-sensitive applications and LLM-based approaches (GPT-4o, Claude) for complex extraction and summarisation tasks. Every NLP system is trained or fine-tuned on your specific domain vocabulary and integrates with your existing data pipeline or operational applications.
Text is your most underused data source
Most businesses are swimming in unstructured text: support tickets, customer emails, contracts, product reviews, clinical notes, compliance documents. Structured data in databases gets analysed. Unstructured text sits in folders and inboxes.
NLP systems turn that text into structured signals -- classifications, scores, extracted entities, summaries -- that your dashboards, CRMs, and operations systems can act on.
What we build
Document classification
Automatic categorisation of incoming documents, emails, and tickets into defined categories. Support ticket routing by issue type and urgency. Legal document classification by agreement type, jurisdiction, or risk level. Financial transaction categorisation. Medical record classification by specialty or document type. Outputs routed to the right queue, workflow, or system without human triage. Accuracy benchmarks provided on your data before deployment.
Named entity extraction
Structured data extraction from unstructured text: parties and signatures from contracts, diagnoses and medications from clinical notes, company names and amounts from financial documents, product specifications from supplier sheets. Custom entity types trained on your domain vocabulary. Output delivered as structured JSON to your database or downstream system -- replacing manual data entry at scale.
Sentiment and intent detection
Customer sentiment scoring on reviews, support conversations, and feedback. Intent classification for chatbots and support routing (billing question vs. technical issue vs. churn risk). Urgency detection for support ticket prioritisation. Brand perception analysis across review platforms and social channels. NPS driver analysis -- which specific topics drive promoters and detractors. Delivered as scores and labels, not just summary statistics.
Text summarisation
Automated summarisation of long documents: contract key term extraction, clinical note summarisation for physicians, research paper summaries, legal brief synthesis, and earnings call highlights. Executive summaries generated at scale without manual review. Medical record summarisation that surfaces relevant patient history before a clinical encounter. Configurable summary length and focus depending on the reader's needs.
Multilingual NLP
NLP models that handle multiple languages from your customer base or document sources. Multilingual classification and extraction without separate models per language. Translation-based pipelines for cross-language analytics. Language detection and routing for multilingual support systems. Particularly relevant for global e-commerce, international financial services, and multinational enterprise applications.
NLP for compliance and legal
Clause extraction and risk flagging in contracts and agreements. Regulatory document analysis for compliance requirement identification. Policy document comparison and change detection. Litigation document review assistance for legal teams. Jurisdiction-specific entity extraction. NLP systems designed for legal and regulatory workflows include audit trails and confidence scoring appropriate for high-stakes document environments.
Tell us about your text data problem.
Document types, current volume, what you need to extract or classify, and where the output needs to go. We'll give you a fixed-cost proposal.
Related services
Machine Learning Development -- ML systems beyond NLP including structured data models
Intelligent Document Processing -- full document workflow automation combining OCR and NLP
AI Document Intelligence -- AI-native document workflows
Generative AI Integration -- LLM integration for text generation and summarisation
Custom AI Development -- AI-native products from scratch
Frequently asked questions
NLP development is building systems that process and understand human language -- classifying text into categories, extracting specific information from documents, detecting sentiment and intent, summarising long content, and translating between languages. Custom NLP development means training or fine-tuning models on your specific data and domain rather than using generic pre-trained models with limited customisation. Custom models significantly outperform generic ones on domain-specific vocabulary: medical terminology, legal language, technical product descriptions, or financial jargon all require domain adaptation to achieve production-grade accuracy.
Traditional NLP (fine-tuned BERT, RoBERTa, SpaCy) is faster, cheaper per inference, and more suitable for high-volume applications where latency and cost are constraints. These models are trained on labelled data and excel at structured classification and extraction tasks. LLM-based NLP (GPT-4o, Claude, Gemini) is more flexible, handles complex reasoning and nuance, and requires fewer labelled examples to achieve good performance. It is better for complex extraction, summarisation, and tasks where the output needs to explain reasoning. We choose the right approach based on your volume, latency requirements, accuracy targets, and cost constraints.
For fine-tuned classification models (BERT-based), 500--5,000 labelled examples per class typically delivers production-grade accuracy. For named entity recognition (extracting specific fields from documents), 200--2,000 annotated documents. LLM-based approaches via few-shot prompting require as few as 10--50 examples to demonstrate the pattern. The right approach depends on your existing labelled data volume -- we assess this during scoping and recommend the most cost-effective path.
Document classification (routing support tickets, classifying legal documents, categorising financial transactions), named entity extraction (extracting parties, amounts, dates, and clauses from contracts; extracting diagnoses and medications from clinical notes), sentiment and intent detection (customer feedback analysis, support ticket urgency scoring, product review analysis), text summarisation (long document summaries for executives, clinical note summarisation, contract key term extraction), and language translation and normalisation (standardising product descriptions, translating multilingual customer feedback).
NLP models are deployed as REST APIs. Your existing application sends text input and receives structured output -- a classification label, an extracted entity list, a sentiment score, or a generated summary. For batch processing, we build pipeline integrations that process document queues and write results to your database or data warehouse. Integration with CRM, support platforms, document management systems, and BI tools is standard. The model runs as a microservice and connects to your stack via API.
A focused NLP system for a single task (document classification or entity extraction) with model training, validation, and API deployment typically runs $20,000--$50,000. Multi-task NLP platforms with pipeline integration and multiple extraction models run $50,000--$120,000. LLM-based implementations using prompt engineering and RAG run lower ($15,000--$35,000) with higher monthly inference costs. We scope every project before pricing.